Displays

We introduce a unique set of global observation-based climatologies of daily precipitation (P) occurrence (related to the lower tail of the P distribution) and peak intensity (related to the upper tail of the P distribution). The climatologies were produced using Random Forest (RF) regression models trained with an unprecedented collection of daily P observations from 93,138 stations worldwide. Five-fold cross-validation was used to evaluate the generalizability of the approach and to quantify uncertainty globally. The RF models were found to provide highly satisfactory performance, yielding cross-validation coefficient of determination (R²) values from 0.74 for the 15-year return-period daily P intensity to 0.86 for the >0.5 mm d^-1 daily P occurrence. The performance of the RF models was consistently superior to that of state-of-the-art reanalysis (ERA5) and satellite (IMERG) products. The highest P intensities over land were found along the western equatorial coast of Africa, in India, and along coastal areas of Southeast Asia. Using a 0.5 mm d^-1 threshold, P was estimated to occur 23.2 % of days on average over the global land surface (excluding Antarctica). The climatologies including uncertainty estimates will be released as the Precipitation DISTribution (PDIST) dataset via www.gloh2o.org/pdist. We expect the dataset to be useful for numerous purposes, such as the evaluation of climate models, the bias correction of gridded P datasets, and the design of hydraulic structures in poorly gauged regions.

How to cite: Beck, H., Westra, S., and Wood, E.: Global observation-based climatology of precipitation occurrence and peak intensity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7837, https://doi.org/10.5194/egusphere-egu2020-7837, 2020.

Since the past decade, evidence derived from model reanalysis (including outgoing longwave radiation, tropopause height, the latitude where zonal mean precipitation exceeds evaporation, and the latitude where the zonal mean 500-hPa meridional streamfunction crosses from positive to negative) indicate that the tropics have been expanding since at least 1979, by a very approximate one degree per decade. To the reanalysis evidence, we have added our direct analysis of near-surface wind estimated from satellite radar scatterometty. These show a widening of the Hadley circulation, with a distinct poleward migration of the zonally-averaged crossing latitudes (from easterly trade winds in the tropics to the mid-latitude westerly winds) by about 1 degree per decade. This begs the question: are the precipitation patterns changing accordingly? The brief answer, derived from analysis of the Tropical Rainfall Measuring Mission radar data, is that deep storm top heights in the tropics showed a monotone increase over the 16-year TRMM record, but their occurrences became steadily less frequent. This will be described in more detail, along with a method to increase the sample size from the rather poor temporal sampling by the TRMM radar to a 50-fold larger sample from the microwave radiometer constellation.

How to cite: Haddad, Z., Hristova-Veleva, S., and Utsumi, N.: Decadal trends in convection from satellite microwave observations of near-surface wind and deep precipitating clouds, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1275, https://doi.org/10.5194/egusphere-egu2020-1275, 2020.

Precipitation is an important input for hydrological models. Uncertainty in its spatiotemporal variability is a major error source for forecasts generated with distributed hydrological models, because this uncertainty propagates non-linearly into simulated soil moisture patterns, groundwater table depths, discharge and surface energy flux partitioning. Thus, it is imperative to use accurate rainfall datasets that reproduce rainfall's intrinsic highly-spatiotemporal variability to obtain better forecasts from hydrological models.

In this study, we present the evaluation of the high-resolution precipitation product RADKLIM against precipitation from the COSMO-DE analysis over the Rur catchment, in western Germany, at a decadal time scale (2007-2015). RADKLIM is the climate version of the quantitative precipitation estimation product RADOLAN developed by the German national weather service (DWD, Deutscher Wetterdienst) by adjusting radar-derived estimates to gauge observations. Its spatiotemporal resolution is ~1x1 km and 5 minutes. The hourly COSMO-DE analysis precipitation data is obtained from the German weather forecast model (also available from DWD) with a spatial resolution of ~2.8x2.8 km. To make a scale-consistent comparison, the RADKLIM product was upscaled to the COSMO-DE resolution.

Overall, the COSMO-DE analysis yields over the studied area 50% more of the average precipitation of the RADKLIM product. The highest biases (COSMO-DE over RADKLIM) predominantly occur during afternoon (i.e., 15:00 - 21:00), and in the summer season; whereas the negative biases predominantly occur during autumn, with their highest in the early afternoon (i.e., 12:00 - 18:00).

How to cite: Rios Gaona, M. F., Shrestha, P., and Simmer, C.: Uncertainty in decadal precipitation estimates over the Rur catchment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8413, https://doi.org/10.5194/egusphere-egu2020-8413, 2020.

The numerical studies reported in the literature about the wind-induced bias of precipitation measurements assume that turbulence is only generated by the interaction of the airflow with the gauge body, and the associated CFD simulations are generally performed under the hypothesis of steady and uniform incoming airflow. However, wind is turbulent in nature due to the roughness of the site and the presence of obstacles so that, in operational conditions, precipitation gauges are immersed in a turbulent flow. In this work, further to the role of the local generation of turbulence due to the obstruction to the airflow caused by the bluff body nature of the precipitation gauge, the natural free-stream turbulence inherent to the wind, and its influence on precipitation measurements, are investigated. With the aim to obtain turbulence intensity values characterizing the wind near to the ground surface, 3-D sonic anemometer measurements at the gauge collector height were preliminarily analyzed. Data were kindly provided by Environmental Measurements Ltd. (EML) from the Nafferton (UK) experimental site and are composed of 38 minutes of high-frequency (20 Hz) wind measurements. The role of the free-stream turbulence on the collection performance of a chimney shaped gauge was investigated by performing Large Eddy Simulations (LES) both in uniform and turbulent free-stream conditions. The free-stream turbulence was generated by introducing geometrical obstacles upstream of the gauge and their distance from the gauge, along the longitudinal direction, was calibrated to obtain the desired level of turbulence intensity, as measured at the Nafferton site. The two free-stream turbulence conditions were compared in terms of catch ratios and collection efficiency. Catch ratios for dry snow particles were obtained by running a literature Lagrangian Particle Tracking model (Colli et al. 2015) applied to the LES airflow fields obtained for each free-stream turbulence condition. From the comparison, a stronger undercatch emerges for small size particles (less than 2mm) under turbulent free-stream conditions with respect to the uniform case, while the opposite occurs for larger particles (d > 2 mm). This is due to the higher attitude of the small size particles to follow the turbulent velocity fluctuations while larger particles are more inertial. The overall effect of the free stream turbulence on the collection performance of the gauge was quantified by computing the Collection Efficiency (CE) as the integral over the full range of particle diameters after assuming a suitable Particle Size Distribution (PSD) for the precipitation process. Results show that a higher CE is obtained under turbulent free-stream conditions, and demonstrated that the numerical derivation of correction curves for use in precipitation measurements as proposed in the literature based on the simplifying assumption of uniform free-stream conditions is affected by a systematic overestimation of the wind-induced error.

References:

Colli, M., Lanza, L.G., Rasmussen, R., Thériault, J.M., Baker, B.C. & Kochendorfer, J. An improved trajectory model to evaluate the collection performance of snow gauges.  Journal of Applied Meteorology and Climatology, 2015, 54, 1826–1836.

How to cite: Cauteruccio, A., Colli, M., and Lanza, L. G.: The role of free-stream turbulence on the collection performance of catching type precipitation gauges, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18366, https://doi.org/10.5194/egusphere-egu2020-18366, 2020.

A novel method for the estimation of surface precipitation using passive observations from the GPM constellation is proposed. The method, which makes use of quantile regression neural networks (QRNNs), is shown to provide a more accurate representation of retrieval uncertainties, high processing speed and simplifies the integration of ancillary data into the retrieval. With that, it overcomes limitations of traditionally used methods, such as Monte Carlo integration as well as standard usage of machine learning.

The bulk of precipitation estimates provided by the Global Precipitation Measurement mission (GPM) is based on passive microwave observations. These data are produced by the GPROF algorithm, which applies a Bayesian approach denoted as Monte Carlo integration (MCI). In this work, we investigate the potential of using QRNNs as an alternative to MCI by assessing the performance of both methods using identical input databases.

The methods agree well regarding point estimates, but QRNN provides better estimates of the retrieval uncertainty at the same time as reducing processing times by an order of magnitude. As QRNN gives more precise uncertainty estimates than MCI, it gives an improved basis for further processing of the data, such as identification of extreme precipitation and areal integration.

Results so far indicate that a single network can handle all data from a sensor, which is in contrast to MCI where observations over oceans and different land types have to be treated separately. Moreover, the flexibility of the machine-learning approach opens up opportunities for further improvements of the retrieval: ancillary information can be easily incorporated and QRNN can be applied on multiple footprints, to make better use of spatial information. The effects of these improvements are investigated on independent validation data from ground-based precipitation radars.

QRNN is here shown to be a highly interesting alternative for GPROF, but being a general approach it should be of equally high interest for other precipitation and clouds retrievals.

How to cite: Eriksson, P., Pfreundschuh, S., Norrestad, T., and Kummerow, C.: A machine learning approach for faster and more accurate precipitation retrievals, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17559, https://doi.org/10.5194/egusphere-egu2020-17559, 2020.

Heavy precipitation events (HPEs) can lead to natural hazards (floods, debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological and societal effects of HPEs. Thus, a correct characterization and prediction of rainfall patterns is crucial for coping with these events. However, information from rain gauges suitable for these goals is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients and small precipitating systems. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. In this study we characterize rainfall patterns during HPEs based on high-resolution weather radar data and evaluate the performance of a high-resolution (1 km²), convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year long radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterized by the highest rain intensities; however, for short storm durations, the highest rain intensities were characterized for the inland desert. During the rainy season, center of mass of the rain field progresses from the sea inland. Rainfall during HPEs is highly localized in both space (<10 km decorrelation distance) and time (<5 min). WRF model simulations accurately generate the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.

How to cite: Morin, E., Armon, M., Marra, F., Enzel, Y., and Rostkier-Edelstein, D.: Radar-based characterization of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7239, https://doi.org/10.5194/egusphere-egu2020-7239, 2020.

The measurement of space-time rainfall fields is of great importance for several purposes including weather forecast, water resource management, evaluation of hydrological risk and monitoring of hydrologic extremes. Conventional methods for rainfall measurement include rain gauges and weather radars, both types of sensor having their own advantages and limitations. A different and not fully tested methodology exploits the power loss (namely, the attenuation) experienced by microwave radio signals when travelling across rain along either terrestrial or ground-to-satellite links. Indeed, it is well known that microwave attenuation due to rain can be calculated from the rain rate along the propagation path. The inverse problem can be solved once the disturbances affecting the radio signal and not induced by rain have been identified and removed.

In this contribution, we present the first results of the experimental campaign carried out in the framework of MOPRAM (MOnitoring Precipitation through a Network of RAdio links at Microwaves). MOPRAM is a scientific project funded by Fondazione Cariplo, which aims at assessing the potential of Commercial Microwave Links (CML) for rainfall estimates and rainfall field retrieval in areas of hydrological interest located in Northern Italy. A network of CMLs, owned by a major mobile operator in Italy, has been exploited to estimate the average rain rate along each link. The available data are the minimum and maximum values of the transmitted and received signal power across each link (two-ways), measured during 15-min time slots.

We start by describing the procedure adopted for the estimation of the baseline, i.e. the received power level immediately before and after a precipitation event. Rain attenuation is subsequently calculated by subtracting the received power during the event from the baseline level. To this aim, every 15-min slot of each link is classified in advance as either wet or dry by taking advantage of the spatial correlation of rain. Path-averaged rainfall intensity can be retrieved from rain attenuation by formulas based on well-known electromagnetic models. Finally, CML-based rainfall is compared with the one obtained from co-located rain gauges and disdrometers.

Despite the CML setup is not optimized for rainfall measurements, preliminary results highlight a good correlation between the occurrence of wet periods detected by CMLs on one side and by rain gauges and disdrometers on the other, as well as a fair agreement between the corresponding time series of accumulated precipitation.

How to cite: Nebuloni, R., D'Amico, M., Cazzaniga, G., De Michele, C., and Deidda, C.: Rainfall estimate using Commercial Microwave Links (CML): first outcomes of the MOPRAM project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10407, https://doi.org/10.5194/egusphere-egu2020-10407, 2020.

The Version 06 Global Precipitation Measurement (GPM) mission products were completed over the last year, capping five years of development since the launch of the GPM Core Observatory, and covering the joint Tropical Rainfall Measuring Mission (TRMM) and GPM eras with consistently processed algorithms.  The U.S. GPM team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product enforces a consistent intercalibration for all precipitation products computed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- and GPM-era calibrators, respectively, and incorporates monthly surface gauge data in the Final (research) product.  Mid-latitude calibrations during the TRMM era necessarily are more approximate because TRMM only covered the latitude band 35°N-S, while GPM covers 65°N-S.  Starting in V06, IMERG employs precipitation motion vectors (used to drive the quasi-Lagrangian interpolation, or “morphing”) that are computed by tracking the vertically integrated vapor as analyzed in MERRA2 and GEOS FP.  This approach covers the entire globe, expanding coverage beyond the 60°N-S latitude band provided by IR-based vectors in previous versions, although we choose to mask out microwave-based precipitation over snowy/icy surfaces as unreliable.

We will provide examples of performance for the V06 IMERG products, including comparison with the long-term record of GPCP and TMPA, showing higher values by about 8% in the latitude band 50°N-S over oceans; diurnal cycle, demonstrating improvement over previous versions; and daily precipitation PDFs for the entire record, showing a shift at the TRMM/GPM boundary, as well as interannual variations.  These analyses have important implications for the utility of V06 IMERG data for long-record calculations.  Finally, we will review the retirement of the predecessor TMPA multi-satellite dataset.

How to cite: Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: IMERG Multi-Satellite Products Across Two Decades, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5282, https://doi.org/10.5194/egusphere-egu2020-5282, 2020.

The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States.  GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar’s retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors.  In May of 2017, GPM released Version 5 of its precipitation products starting with GMI and continuing with the constellation of radiometers.  The precipitation products from these sensors are consistent by design and show relatively minor differences in the mean global sense.  Since this release, the Combined Algorithm hydrometeor profiles have shown good consistency with surface observations and computed brightness temperatures agree reasonably well with GMI observations in precipitating regions.  The same is true for MIRS profiles in non-precipitating regions.  Version 7 of the GPROF code will therefore make use of these operational products to construct it's a-priori databases.  This will allow continuous improvements in the a-priori database as these operational products are reprocessed with newer versions, while allowing the user community to better focus on the algorithm’s error covariance matrix and its validation.  Results from early versions of this algorithm will be presented.  In addition to creating an a-priori database that can be more directly updated with improvement to the raining and non-raining scenes, GPROF is also undertaking steps to improve the orographic representation of snow and a Neural Network based Convective/Stratiform classification of precipitation that will both help improve instantaneous correlations with in-situ observations.

How to cite: Kummerow, C. and Brown, P.: The GPM Operational Radiometer Algorithm - Changes for 2021, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1277, https://doi.org/10.5194/egusphere-egu2020-1277, 2020.

The Global Precipitation Measurement (GPM) mission is an international collaboration to achieve highly accurate and highly frequent global precipitation observations. The GPM mission consists of the GPM Core Observatory jointly developed by U.S. and Japan and Constellation Satellites that carry microwave radiometers and provided by the GPM partner agencies. The GPM Core Observatory, launched on February 2014, carries the Dual-frequency Precipitation Radar (DPR) by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT).

JAXA is continuing DPR data monitoring to confirm that DPR function and performance are kept on orbit. A scan pattern of the DPR was changed in May 2018. The next product applying the new scan pattern will be released as an experimental product (V06X) in 2020. The DPR follow-on mission has been actively discussed in Japan.

JAXA also develops the Global Satellite Mapping of Precipitation (GSMaP), as national product to distribute hourly and 0.1-degree horizontal resolution rainfall map. GSMaP has been used for various research fields and JAXA keeps it developed and improved, in cooperation with domestic/international partner agencies.

The GSMaP near-real-time version (GSMaP_NRT) product provides global rainfall map in 4-hour after observation, and recently GSMaP near-real-time gauge-adjusted version (GSMaP_Gauge_NRT) product has been published. The higher priority to data latency time than accuracy leads to wider utilization by various users for various purposes, such as rainfall monitoring, flood alert and warning, drought monitoring, crop yield forecast, and agricultural insurance.

Improved GSMaP_Gauge_NRT product (v6) was open to the public in Dec. 2018. Correction coefficients are calculated using past 30 days based upon Mega et al. (2019)’s method. We completed reprocessing of past 19yr data record (since Mar. 2000). Validations with reference to the JMA radar around Japan show smaller RMSEs in this new product than the current NRT (no gauge-correction).

JAXA started to provide the GSMaP real-time product called GSMaP_NOW by using the geostationary satellite Himawari-8 operated by the Japan Meteorological Agency (JMA) since November 2015. Recently, the domain of GSMaP_NOW was extended to the global region in June 2019. Furthermore, we developed the gauge-adjusted real-time version, GSMaP_Gauge_NOW, which was also released in June 2019. In the method, estimates from the GSMaP_NOW are adjusted using an optimization model (Mega et al. 2019) with parameters calculated from the GSMaP_Gauge (gauge-adjusted standard version) during the past 30 days.

GSMaP products can be seen via website and easy to monitor the global rainfall with good latency. GSMaP since March 2000 up to 4-hour after observation is available from the “JAXA Global Rainfall Watch” website (https://sharaku.eorc.jaxa.jp/GSMaP/index.htm); while GSMaP_NOW product is from the "JAXA Realtime Rainfall Watch" web site (https://sharaku.eorc.jaxa.jp/GSMaP_NOW/index.htm).

How to cite: Furukawa, K., Kubota, T., Yamaji, M., Tashima, T., Kaneko, Y., Yamamoto, K., Oki, R., Takahashi, N., and Takayabu, Y.: Recent status of the Dual-frequency Precipitation Radar (DPR) and the Global Satellite Mapping of Precipitation (GSMaP) in the Global Precipitation Measurement (GPM) mission, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4282, https://doi.org/10.5194/egusphere-egu2020-4282, 2020.

Dual-frequency radars have been increasingly used for detecting and retrieving cloud and precipitation, such as the Ku- and Ka-band Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core satellite. The objective of this study is to evaluate performance of the standard dual-frequency technique, which uses the differential frequency ratio (DFR), defined as the difference of radar reflectivities between two wavelengths, for the estimation of snow microphysical properties and the associated bulk parameters from Ku- and Ka-band as well as Ka- and W-band dual-frequency radars. Although the DFR-based technique is effective in obtaining snow properties, its retrieval accuracy depends on the model assumptions, which include parameterization of particle size distribution (PSD), empirical mass-size relation that links the observed geometrical size of particle to its mass, and the radar scattering model. The complex nature of snowflakes regarding shape, structure, and the inability of the modeled PSD to represent actual snow spectra, lead to errors in the estimates of snow parameters. Additionally, uncertainties associated with scattering computations of snowflakes also affect the accuracy of the dual-wavelength radar retrieval of snow. Therefore, understanding the uncertainties in snow precipitation estimation that depend on PSD parameterizations and scattering models of individual particles is important in evaluating the overall performance of dual-frequency retrieval techniques. Furthermore, separation of the uncertainties associated with the PSD models and the scattering models and their respective contributions to overall uncertainties are useful for gaining insight into ways to improve the retrieval methods.

Snow PSD is usually modelled as a gamma distribution with 2 or 3 free parameters depending on whether its shape factor is fixed or taken as a function of D_m. In this study, our focus is on an assessment of the uncertainties in snow estimates arising from the PSD parameterization and the mass-size relation. To do this, measured PSD data are employed. The snow mass spectra, which can be converted from measured PSD using an empirical mass-size relation, are used to obtain PSD parameters, e.g., the liquid-equivalent mass-weighted diameter (D_m) and the normalized intercept of a gamma PSD (N_w), and the snow bulk parameters, such as snow water content (SWC) and liquid-equivalent snowfall rate (R) if a measured fall velocity-size relationship is utilized. Coupling measured PSD with particle scattering model, measured radar parameters can be computed, which are subsequently used as inputs to the standard dual-frequency algorithm. An evaluation of the retrieval accuracy is conducted by comparing the radar estimates of D_m, N_w, SWC and R with the same quantities directly computed from the PSD spectra (or truth). In this study, measurements of the snow PSD and fall velocity acquired from the Snow Video Imager/Particle Image Probe (SVI/PIP) at the NASA Wallops flight facility site in Virginia are employed. There are several scattering databases available that provide the scattering properties of snow aggregates in accordance with various snow and ice crystal growth models. Variability of the snow estimates caused by the differences of various scattering tables will be analyzed to explore the uncertainties associated with the scattering tables.

How to cite: Liao, L., Meneghini, R., Tokay, A., and Kim, H.: Assessment of Ku- and Ka-band Dual-Frequency Radar for Snow Estimates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9615, https://doi.org/10.5194/egusphere-egu2020-9615, 2020.

In precipitation science, satellite data have been providing precious, fundamental information, while numerical models have been playing an equally important role. Data assimilation integrates the numerical models and real-world data and brings synergy. We have been working on assimilating the GPM data into the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) using the Local Ensemble Transform Kalman Filter (LETKF). We continue our effort on “Enhancing Precipitation Prediction Algorithm by Data Assimilation of GPM Observations” funded by JAXA, following successful completion of the 3-year project titled “Enhancing Data Assimilation of GPM Observations” from April 2016 to March 2019. The project first started in April 2013 on “Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements”, where we developed a global data assimilation system NICAM-LETKF from scratch. This presentation will provide a summary of the past 7-year effort with more emphasis on the recent achievements, including JAXA’s near-real-time analysis called NEXRA (NICAM-LETKF JAXA Research Analysis) and new theoretical developments of Local Particle Filter to treat highly non-Gaussian distributions of precipitation variables in data assimilation.

How to cite: Miyoshi, T., Kotsuki, S., Terasaki, K., Otsuka, S., Chen, Y.-W., Kanemaru, K., Satoh, M., Yashiro, H., Tomita, H., Kondo, K., Okamoto, K., Kalnay, E., and Kubota, T.: Enhancing Precipitation Prediction Algorithm by Data Assimilation of GPM Observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21745, https://doi.org/10.5194/egusphere-egu2020-21745, 2020.

Accurate estimation of precipitation is essential in weather prediction, climate change research, and hydrologic applications. However, unlike temperature and pressure, precipitation fields can be spatially patchy and consequently extremely difficult to measure and predict. Many efforts have been made to measure precipitation since the 18th century, but building a global, consistent, and continuous database of rainfall is still challenging. The launch of the Global Precipitation Measurement Core Observatory (GPM-CO) in February 2019 emerged as a promising alternative to measure precipitation at global scale. After five years in orbit, the GPM Mission has produced enough quality-controlled data to allow a validation of their precipitation estimates over Europe.

This study evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG) over Europe in order to evaluate application of the retrievals to hydrology. IMERG is compared with a pan-European precipitation dataset built on rain gauge stations, the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R² =0.8), important discrepancies are revealed in mountainous regions, specifically the Pyrenees, the Alps, west coast of the British Isles, Scandinavia, the Italian and Iberian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG has no gauges available for adjustment. If rain gauges are available, IMERG yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG overestimates precipitation in the British Isles, Adriatic coastline, Italian Peninsula, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data (GPCC).

How to cite: Navarro, A., García-Ortega, E., Sánchez, J. L., Merino, A., Kummerow, C., and Tapiador, F. J.: Assessments of IMERG precipitation over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2947, https://doi.org/10.5194/egusphere-egu2020-2947, 2020.

Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water.  This paper will highlight the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) satellite observatory.  Both TRMM and GPM are providing four-dimensional account of rainfall and its associated precipitation properties over the global Tropics and mid-latitudes: information that can be used to estimate the space-time structure of latent heating.

Goddard Convective-Stratiform or CSH retrieved LH is one of two standard LH products (the other one is from Japan Spectral Latent Heating or SLH). This paper will present (1) the new improvements of the CSH LH algorithm by better CRM simulated LH for its look-up table, and (2) the performance of CSH retrieved LH by comparison with surface rainfall rate.  In addition, the similarities and differences of CSH retrieved LH obtained from TRMM and GPM measurements, respectively, will be presented.  At the end of presentation, the further research on latent heating retrieval from satellites will be discussed.

How to cite: Tao, W.-K., Lang, S., and Iguchi, T.: Latent Heating Retrieved from TRMM and GPM Satellite Measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3096, https://doi.org/10.5194/egusphere-egu2020-3096, 2020.

This paper presents a configuration of a novel, airborne phased array radar (APAR) motivated by major advances in cellular technology, component miniaturization, and radar antenna simulation software. This has paved the way for a next-generation radar being designed by NCAR/EOL to be installed on the NSF/NCAR C-130 aircraft. The APAR system will consist of four removable C-band active electronically scanned arrays (AESA) strategically placed on the fuselage of the aircraft. Each AESA measures approximately 1.5 x 1.5 m and is composed of 2368 active radiating elements arranged in a total of 37 line replaceable units (LRU). Each LRU is composed of 64 radiating elements that are the building block of the APAR system.

Polarimetric measurements are not available from current airborne tail Doppler radars. However, APAR, with dual-Doppler and dual polarization diversity at a lesser attenuating C-band wavelength, will further advance the understanding of the microphysical processes within a variety of precipitation systems. Such unprecedented observations, in conjunction with the advanced radar data assimilation schema, will be able to address the key science questions to improve understanding and predictability of significant weather.

A Mid-scale Research Infrastructure proposal is submitted to the National Science Foundation to request the implementation cost. The development is expected to take ~5 years after the funding is in place. It adopts a phased approach as an active risk assessment and mitigation strategy. At the present time, both the National Science Foundation and the National Oceanic and Atmospheric Administration are funding the APAR project for risk reduction activities. The APAR Team is actively seeking partners in industry and in the university community. An APAR science and engineering advisory panel has been organized.

The authors will review the overall design and current progress of APAR and outline ambitious future development work needed to bring this exceptional tool into full operation.

How to cite: Grubišić, V., Lee, W.-C., and Lussier, L. L.: Airborne Phased-Array Radar (APAR): The Next Generation of Airborne Polarimetric Doppler Weather Radar, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18421, https://doi.org/10.5194/egusphere-egu2020-18421, 2020.

Representation of the diurnal cycle is a key trial of the ability of models to capture precipitation timing, duration, and intra-daily variations.  The state-of-the-art model simulations from the Coupled Model Intercomparison Project (CMIP6), which are set to inform the upcoming IPCC sixth assessment report, are yet to be compared to the diurnal cycle of precipitation according to observations.  The recently released version 6 of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) product provides over 19 years of global-gridded observations (June 2000 - Present).  Such state-of-the-art observations, with inputs from space-borne dual-frequency radar, microwave radiometers, infrared sensors and ground-based gauges, have never been available at 0.1˚ gridding every half hour over such a long period.  This study aims to compare the amplitude and time of maximum precipitation accumulation between IMERG observations and CMIP6 models over an 8-year period (June 2000 – May 2008).  Preliminary results suggest that the CMIP6 models typically underestimate the amplitude of precipitation accumulation over land compared to observations, though there are overestimates in the Amazon and across central Africa.  Furthermore, the CMIP6 models typically lag behind observations in their time of maximum accumulation over land; observations suggest a late evening to night maximum whilst CMIP6 models show a late morning to early afternoon maximum.  The results will be beneficial to improving modelling of precipitation across the globe.

How to cite: Watters, D., Battaglia, A., and Allan, R.: The Diurnal Cycle of Precipitation: A Comparison of State-of-the-Art Observations and Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18910, https://doi.org/10.5194/egusphere-egu2020-18910, 2020.

Snowfall remote sensing  is becoming an increasingly popular topic within both the scientific community and operational services. Studies focused on snow retrievals are important because snow represents a reservoir of fresh water and its quantification is a crucial task to thoroughly understanding the hydrological cycle. In addition, snow-cover plays a key role in the climate system, modifying the global energy budget because of its high albedo. In addition, snowfalls often represent a hazard to several public services (e.g. transportations, energy providers) as well as properties (e.g. roof loading) but also an opportunity (e.g. for hydropower).

Passive microwave observations provided by currently operating spaceborne radiometers (e.g. Advanced Technology Microwave Sounder (ATMS), the Global Precipitation Measurement (GPM) Microwave Imager (GMI)) are a unique source of global information on the occurrence and the quantity of snowfall. However, because of the weaker and more complex signatures of snow at microwave frequencies [1] compared to those from rainfall, the retrieval schemes used by such instruments are still not fully optimised for snowfall detection and estimation, and subject to large errors. The ESA-funded RAINCAST project aims, among other tasks, at the verification of passive microwave snowfall products with the goal of fostering and defining new retrieval algorithms and mission concepts specifically optimised for snowfall quantification.

In this study we show a comparative analysis between passive microwave snowfall rate estimates and high quality ground-based radar snowfall measurements to quantify the actual strengths and limitations in state-of-the-art passive microwave snowfall products. In particular, the performance of the Goddard profiling algorithm version 5A (GPROF V5A) and of a recently developed snowfall retrieval algorithm for GMI named SLALOM [2, 3] are investigated. The differences between GPROF and SLALOM are explored in relation to the environmental conditions (including the presence of supercooled droplets aloft that tend to mask the typical snowfall signature) where the snowfall retrievals are likely less accurate. In addition, ATMS snowfall products are analysed as well for selected case studies to evidence the potential and limitations of the different snowfall products in relation to the algorithm’s design (e.g., GPROF vs. SLALOM) and sensor characteristics (GMI and ATMS). Then quantitative assessments for all products are discussed by exploiting one year of ground reference radar network data over Northern U.S. and Canada provided by the Multi-Radar/Multi-sensor System (MRMS) product, available at 1x1 km horizontal regularresolution and 2 min time sampling, and providing gauge adjusted surface precipitation rate together with the indication of its phase.

Our analysis confirms results from recent work on the same topic [e.g., 4], although a long term large scale analysis that quantify passive microwave retrieval is not found in the past literature. 

This work is particularly relevant not only for the quantification of the limitations of the current snowfall retrieval algorithms, but also to give recommendations for algorithm development for upcoming satellite missions (e.g. EPS-SG MWS, MWI/ICI), and for future satellite mission concepts.

REFERENCES

[1] Liu, G. et al, 2008. doi:  10.1029/2007JD009766.

[2] Rysman, J.-F. et al., 2018. doi: 10.3390/rs10081278

[3] Rysman J.-F., et al., 2019. doi:10.1029/2019GL084576,

[4] Von Lerber, et al. doi: /10.1175/JAMC-D-17-0176.1

How to cite: Montopoli, M., Mroz, K., Panegrossi, G., Casella, D., Baldini, L., Sanò, P., Camplani, A., Laviola, S., Kirstetter, P., Kulie, M., and Battaglia, A.: Verification study of passive microwave snowfall products using ground-based radar network observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19460, https://doi.org/10.5194/egusphere-egu2020-19460, 2020.

Modern ways to measure rainfall provide a variety of different solutions, direct and indirect, with respect to the standard approach that is the raingauge method. Retrieving the actual rain fallen on a target domain is not in fact as easy task due to its temporal and spatial variability, but its importance is paramount for meteorology and for the effects on human lives and the environment. Rainfall regimes are changing almost at every latitude with dramatic effects, with a complex connection to climate change in large part to be still understood.

Among the emerging new methods for rainfall estimation, a specific interest is in the so-called ‘opportunistic’ measurements, because they provide a chance to augment information without adding new infrastructures, also with clear cost advantages. These data are of course less precise than those from dedicated instruments. Therefore some smart efforts in devising proper processing are needed to extract all the geophysical information that they can provide. Use of microwave links in cellular phone networks is among these methods, bringing information on rainfall rates along their path through signal attenuation caused by raindrops. Following a similar principle also broadcast telecommunication satellite signals can be used, with additional problems related to the definition of the intercepted precipitation volumes and the effects of the melting layer, but additional advantages related to the worldwide availability of the signal and the easiness of data acquisition, that can be natively centralised when using two ways communication receivers. NEFOCAST, a research project funded by the regional administration of Tuscany (Italy), exploited this feature through new two-way (transmit-receive) devices named SmartLNB (Smart Low-Noise Block converter), that are going to constitute networks of sensors of opportunity, densely distributed especially in urbanised areas. Two-way receivers allow both to estimate and relying attenuation data that can be centralized to be processed for real-time rainfall estimation, every minute.

An experimental network of SmartLNBs has been deployed in Italy (namely Florence, Pisa and Rome), including co-located raingauges and radar measurements for cal/val objectives. SmartLNBs provide average measurements along quasi-parallel non-nadir paths, so that information on the structure of the intercepted rainfall system is needed in order to retrieve ground precipitation. The high rate of measurements provided by the SmartLNBs suggested to approach the rainfall retrieval problem similarly to a trajectory assessment in a phase space, using an ensemble Kalman filter to produce the rainfall field over a given domain. MSG satellite precipitation products can be used for the purpose and also as initial and boundary conditions, while atmospheric motion vectors from the same data source are used in the propagation model of the Kalman filter.

In this work, we present the measurement concept, the signal processing algorithm and the method to retrieve the rainfall fields, through some significant synthetic and real case studies, for events with different intensity, dynamics and morphology and for various sensor distributions.

How to cite: Ortolani, A., Caparrini, F., Melani, S., Antonini, A., Mazza, A., Baldini, L., Giannetti, F., Facheris, L., and Vaccaro, A.: Real-time rainfall maps from satellite telecommunication signals, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19722, https://doi.org/10.5194/egusphere-egu2020-19722, 2020.

The utilization of satellite observations in the estimation of global precipitation is now well established. However, quantifying the errors and uncertainties associated with such estimates is very much in its infancy. While many validation studies have been undertaken, these tend to provide case-specific or longer-term/large area measures of the performance of the precipitation products: statistical performance has largely taken precedence over an assessment of errors and uncertainties within such products. As the requirements for finer spatial and temporal resolutions increase, the assumptions made on the bulk large area/long time-frame products are no longer appropriate: careful assessments of the apportionment of the errors and uncertainties within the precipitation products needs to be made.

The premise of this study is that to truly understand the errors and uncertainties in the final precipitation product it is essential to quantify these within the elements that make up each individual satellite sensor and precipitation retrieval scheme or algorithm. Thus, we start with two fundamental categories: the observation capability of the sensor and the ability of the retrieval scheme. Each sensor provides different observations resulting from the engineering aspects of the sensor itself through to the sampling regime once the sensor is taking measurements: the observation capability is fixed and will be the same for all the subsequent retrieval schemes. The retrieval schemes themselves have a number of assumptions, both in terms of what the sensor actually observes and in the observation-to-rainfall relationships. While many of the errors and uncertainties associated with these assumptions cannot be easily quantified, the relative magnitude of each can be assessed. Initial results are presented here that quantify the effects of the spatial and temporal sampling of sensors, together with the impact of channel selection upon the final products. These results provide an insight into ability of such techniques to retrieve precipitation from the local to global scales, and how such techniques may be improved in the future.

How to cite: Kidd, C. and Maggioni, V.: Quantifying errors and uncertainties in satellite precipitation estimates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5857, https://doi.org/10.5194/egusphere-egu2020-5857, 2020.

Two products from the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) algorithms, a flag of intense solid precipitation above the –10°C height (“flagHeavyIcePrecip”), and a classification of precipitation type (“typePrecip”) were validated quantitatively from the viewpoint of microphysics using ground-based in-situ hydrometeor measurements and X-band multi-parameter (X-MP) radar observations of snow clouds that occurred on 4 February 2018. The distribution of the “flagHeavyIcePrecip” footprints was in good agreement with that of the graupel-dominant pixels classified by the X-MP radar hydrometeor classification. In addition, the vertical profiles of X-MP radar reflectivity exhibited significant differences between footprints flagged and unflagged by “flagHeavyPrecip”. We confirmed the effectiveness of “flagHeavyIcePrecip”, which is built into “typePrecip” algorithm, for detecting intense ice precipitation and concluded that "flagHeavyIcePrecip" is appropriate to useful for determining convective clouds.

It is well known that the lightning activity is closely related to the convection. We examined the lightning activity using GPM DPR product flagHeavyIcePrecip that indicates the existence of graupel in the upper cloud. On 20 June 2016, we experienced heavy rain with active lightning during Baiu monsoon rainy season, while the GPM DPR passed over Kyushu region in Japan. The distribution of “flagHeavyIcePrecip” obtained from the GPM DPR well corresponded to the CG/IC lightning concentration. On 4 September 2019, isolated thunder clouds observed by the GPM DPR was also similar to the “flagHeavyIcePrecip” distribution. However, partially there was IC lightning without “flagHeavyIcePrecip”, which was positive lightning. It was suggested to have been produced in the upper clouds in which positive ice crystals were dominant.

How to cite: Suzuki, K., Kamamoto, R., Kawano, T., Nakagawa, K., and Kaneko, Y.: Microphysical features indicated by GPM DPR product “flagHeavyIcePrecip” – Case studies on lightning activity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20445, https://doi.org/10.5194/egusphere-egu2020-20445, 2020.

Many cell phone base stations are connected by a network of commercial microwave links (CMLs). At the typically used frequencies between 15 GHz and 40 GHz, precipitation along the path of a CML leads to significant attenuation of the signal. The path-averaged rain rate along a CML can therefore be derived from measurements of the attenuation.

In cooperation with Ericsson, we record attenuation data of 4000 CMLs across Germany with our own open source data acquisition software. The data is acquired every minute and is available to us in real time. The dataset is continuously growing and now spans more than two and a half years. 

Here we present and discuss results from our current processing chain for hourly country-wide CML-derived rainfall fields. We show the effect of improved rain event detection in the raw attenuation time series and the necessity to correct for wet antenna attenuation (Graf et al., 2019). Validation is done via the gauge-adjusted radar product RADOLAN-RW of the German meteorological service. For summer months the pearson correlation between CML and radar data reaches up to 0.85, but is substantially worse during the winter months. The presented processing chain is fast enough to be applied in real-time, which will be illustrated in a live-demo. Furthermore, since Germany has both, a large network of CMLs and a modern weather radar network, we also work on the combination of these data sources. We will present first results of an approach where CMLs are used as an additional source for weather radar rain rate adjustment similarly to the existing gauge-adjustment done in RADOLAN.

Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: Optimized processing and validation for one year of data, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-423, 2019

How to cite: Chwala, C., Smiatek, G., Graf, M., Polz, J., Winterrath, T., and Kunstmann, H.: Current and future CML-rainfall estimation in Germany: Improved data processing, real-time rainfall maps and fusion with weather radar data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15915, https://doi.org/10.5194/egusphere-egu2020-15915, 2020.

The EXCELSIOR project aims to upgrade the existing ERATOSTHENES Research Centre established within the Cyprus University of Technology into a sustainable, viable and autonomous ERATOSTHENES Centre of Excellence (ECoE) for Earth Surveillance and Space-Based Monitoring of the Environment. The ECoE for Earth Surveillance and Space-Based Monitoring of the Environment will provide the highest quality of related services both on the National, European and International levels through the ‘EXCELSIOR’ Project under H2020 WIDESPREAD TEAMING. The vision of the ECoE is to become a world-class Digital Innovation Hub (DIH) for Earth observation and Geospatial Information becoming the reference Centre in the Eastern Mediterranean, Middle East and North Africa (EMMENA) within the next 7 years. The ECoE will lead multidisciplinary Earth observation research towards a better understanding, monitoring and sustainable exploitation and protection of the physical, built and human environment, in line with International policy frameworks. Indeed, the scientific potential of the new upgraded ECoE focusing on the integration of novel Earth observation, space and ground based integrated technologies for the efficient systematic monitoring of the environment. Furthermore, ECoE aims to excel in five domains:  i) Access to energy; ii) Disaster Risk Reduction; iii) Water Resource Management; iv) Climate Change Monitoring and v) Big Earth observation Data Analytics. This will be achieved through research and innovation excellence in the respective scientific and technological disciplines and working together with other Earth observation industries, whereby the ECoE will develop a pool of scientific expertise and engineering capability as well as technical facilities. The partners of the EXCELSIOR consortium include the Cyprus University of Technology as the Coordinator, the German Airspace Center (DLR), the Leibniz Institute for Tropospheric Research (TROPOS), the National Observatory of Athens (NOA) and the Department of Electronic Communications, of the Ministry of Transport, Communications and Works of Cyprus.

The EXCELSIOR project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 857510 and from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development.

How to cite: Hadjimitsis, D., Schreier, G., Kontoes, H., Ansman, A., Komodromos, G., Themistocleous, K., Neocelous, K., Michaelides, S., Mamouri, R., Papoutsis, I., Bühl, J., Schwarz, E., Tziortzis, S., Nisantzi, A., Mettas, C., Papoutsa, C., Danezis, C., and Tzouvaras, M.: The "Excelsior" H2020 Widespread Teaming Phase 2 Project: ERATOSTHENES: EXcellence Research Centre for Earth SurveiLlance and Space-Based MonItoring Of the EnviRonment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21836, https://doi.org/10.5194/egusphere-egu2020-21836, 2020.

Quantitative Precipitation Forecasting (QPF) is among the most central challenges of atmospheric prediction systems. The primary aim of such a task is the generation of accurate estimates of heavy precipitation events associated with severe weather, atmospheric fronts and heavy convective rainfalls. QPF is still among the most intricate challenges of Numerical Weather Prediction. The efforts in this direction are mainly concentrated on improving model formulations for microphysics and convective process and remote sensing data assimilation.

This paper describes the first results with the regional radar signal processing chain that provides the radar data assimilation (RDA) in the Harmonie convection permitting numerical model. This task is performed for a case study focusing on a wintertime frontal cyclone over the island of Cyprus. Reflectivity measurements from two weather radars, at Larnaka and Paphos, are exploited for simulations of severe weather conditions associated with this synoptic-scale system. Through the variational assimilation procedure, the model takes into account the atmospheric processes occurring in the upstream flow which can be outside the area of radar measurements. The focus is on the precipitable water vapor content and its changes during the cyclone evolution, as well as on the impact of the radar data assimilation on precipitation estimates.

The results show that the numerical experiments exhibit, in general, a suitable simulation of precipitable water at different stages of the cyclone. In particular, the bulk of the rainfall volume exhibits three stages: intensive rain on the cyclone's frontal zone, weaker precipitation immediately behind the front, and the secondary enhancement of rainfall. The largest corrections due to RDA are of up to 5 mm and occur during the approach of the cyclone frontal zone in a form of enhanced rainfall over the whole area, but more prominently in weak precipitation locations.

How to cite: Michaelides, S., Ivanov, S., Ruban, I., Charalambous, D., and Tymvios, F.: A Case Study of Weather Radar Data Assimilation into the Harmonie Numerical Weather Prediction System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9350, https://doi.org/10.5194/egusphere-egu2020-9350, 2020.

The motivation behind this research was initially the observation and the subsequent modelling of the gravitational sorting of precipitation in disdrometer-based spectra. The gravitational sorting signature (GSS) is expected to be observed when every drop impact measured by the disdrometer is time tagged and then displayed as a scatter plot diagram of drop diameter (D) versus time (t). The resulting D-t diagrams exhibit marked diagonal features and gravitational sorting signatures are characterized by a negative slope. However, because of the way that manufacturers and researchers process disdrometer data, this signature is typically wiped out. 

This research is based on the assumption that if a rain producing cloud that goes through a complete rain process from start to end, remains fixed (no advection) over a disdrometer site, then some GSS should occur; if advection dominates, then GSS may not be observable.  In this latter case, the precipitating cloud may move over the disdrometer. In this paper, two cases are presented one in which GSS was detected and another in which GSS was absent.

The disdrometer data used in this study were recorded by using a Joss-Waldvogel impact disdrometer located on the roof of a building of the meteorological station at Athalassa, Cyprus (35.15°N, 33.40°, 161.0 m above Mean Sea Level, MSL). The Joss-Waldvogel impact disdrometer used is able to record drop diameters from 0.3mm to 5.5mm in ten-second intervals, allowing for the establishment of the Drop Size Distribution (DSD) representing this range of drop sizes.

How to cite: Kasparis, T., Michaelides, S., and Lane, J.: Disdrometer Gravitational Sorting Signature in a Mediterranean Environment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22356, https://doi.org/10.5194/egusphere-egu2020-22356, 2020.

A recent review about diversities of epiphytes in tropical forests of the Neotropics revealed an unexpected high diversity at lower elevations in an area in French Guiana where the formation of nocturnal radiation fog, intensified by katabatic drainage flows from the surrounding terrain fosters epiphytic growth. Consequently, the new diversity hotspot has been termed ’Tropical Lowland Cloud Forest‘ (TLCF) in analogy to the well-known Tropical Montane Cloud Forests. In this new project funded by the German Research Foundation, we test the hypothesis that the new forest type is widespread in the Tropics if the local terrain allows the formation of nocturnal radiation fog. The presented study is based on satellite data because no operational fog measurements from natural rain forests are available. Since fog in TLCFs is a nocturnal / early morning phenomenon, we use all overflights by the MODIS Aqua platform with 1 km resolution. Fog / low stratus clouds are derived by using a machine learning approach which is trained by MODIS and CALIPSO data. Potential lowland forest areas will be derived from ASTER Global Digital Elevation Model  and Landsat Vegetation Continous Fields

How to cite: Pohl, M.: Spatial delineation of a new fog-driven ecosystem in the tropical lowlands , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7722, https://doi.org/10.5194/egusphere-egu2020-7722, 2020.

In this study, moisture sources for summer (June–July–August) precipitation over eastern China are investigated by performing a transient simulation for the period 1979–2009 using the Lagrangian particle dispersion model FLEXPART. The results show that the Indochinese Peninsula plus southern China, the South China Sea, the northwestern Pacific Ocean, the Asian continent, and the Bay of Bengal are major moisture source regions for summer precipitation over eastern China and that the moisture originated from eastern China, the Arabian Sea, and the Indian subcontinent has minor contributions. The contribution of the oceanic sources significantly surpasses that of the continental sources. The contributions of the various moisture source regions exhibited significant interannual variations during 1979–2009, especially the Indochinese Peninsula plus southern China, the South China Sea, the northwestern Pacific Ocean, the Asian continent, and the Bay of Bengal. Moreover, moisture sources have obvious monthly variations and seasonal cycle features, which are responsible for providing moisture for precipitation in the different stages of the monsoon over eastern China. In addition, it is revealed that a great amount of moisture for the slight precipitation over eastern China originates from the local and northwestern continental regions and eastern oceanic regions adjacent to eastern China, while more moisture comes from southwestern oceanic source regions and their adjacent continental regions for heavy precipitation.

How to cite: Hu, Q.: Moisture sources of summer precipitation over eastern China during 1979–2009: A Lagrangian transient simulation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8123, https://doi.org/10.5194/egusphere-egu2020-8123, 2020.

Wind is recognised as the major environmental source of error in precipitation measurements. For traditional catching type gauges, which are composed by a funnel to collect the precipitation and a container with a bluff body shape, the exposure effect produces the updraft and acceleration of the velocity field in front and above of the collector. These divert the trajectories of approaching hydrometeors producing  a relevant under-catch, which increases with increasing the wind velocity. This problem has been recently addressed in the literature using Computational Fluid Dynamics (CFD) simulations and a Lagrangian Particle Tracking (LPT) model to provide correction curves for various instruments, which closely match the under-catch observed in field measurements.

The present work concentrates on the Hotplate precipitation gauge developed at the Research Applications Laboratory, National Center for Atmospheric Research in Boulder, Colorado. The Hotplate differs from the traditional catching type gauges because it operates by means of an indirect thermodynamic principle. Therefore, it is not equipped with any funnel to collect the precipitation and is composed by a small disk with a diameter of 13 cm with two thin aluminium heated plates on the upper and lower faces. On the plates three concentric rings are installed to prevent the hydrometeors from sliding off during strong wind conditions.

In order to quantify the wind-induced error, the Unsteady Reynolds Averaged Navier Stokes (URANS) equations were numerically solved, with a k-ω SST turbulence closure model, to calculate the airflow velocity field around the instrument. Numerical results were validated by comparison with wind tunnel flow velocity measurements from pressure probes and a Particle Image Velocimetry (PIV) technique.

Then, with the objective to calculate the Collection Efficiency (CE) the hydrometeor trajectories were modelled using a literature LPT model (Colli et al. 2015) that solves the particle motion equation under the effects of gravity and wind. The path of each particle was analysed, considering the complex geometry of the gauge body, to establish whether it is captured by the instrument or not.

For various particle size/wind velocity combinations, the ratio between the number of particles captured by the instrument and the number of particles that would be captured if the instrument was transparent to the wind was calculated. Finally, the CE curve was derived assuming a suitable particle size distribution for solid precipitation.

The results show that the Hotplate gauge presents a very unique response to the wind if compared with more traditional instruments. The CE indeed decreases with increasing the wind speed up to 7.5 m/s, where the effect of geometry starts to overcome the aerodynamic effect, and slowly reverses the trend beyond that value. This effect is so prominent at high wind speed that slightly beyond 15 m/s the under-catch fully disappears and the instrument starts to exhibit a rapidly increasing over-catching bias.

References:

Colli, M., Lanza, L.G., Rasmussen, R., Thériault, J.M., Baker, B.C. & Kochendorfer, J. An improved trajectory model to evaluate the collection performance of snow gauges.  Journal of Applied Meteorology and Climatology, 2015, 54, 1826–1836.

How to cite: Chinchella, E., Cauteruccio, A., Stagnaro, M., Freda, A., and Lanza, L. G.: Evaluation of wind-induced errors for the Hotplate precipitation gauge using computational fluid dynamic simulations., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21543, https://doi.org/10.5194/egusphere-egu2020-21543, 2020.

   In East Asia, a significant subtropical front called the Baiu/Meiyu front appears just before midsummer and brings the huge rainfall there, greatly influenced by the Asian summer monsoon. However, large-scale atmospheric features and rainfall characteristics (such as convective or stratiform rain) as well as the total rainfall amount around the front show rather great differences between the western and eastern portions. For example, in the western part of the Japan Islands (especially around Kyushu District, the most western part) and the Changjiang River Basin in Central China, the more frequent appearance of the heavy rainfall events due to the organized deep convective clouds than in the eastern Japan results in the larger climatological precipitation amount there. This is greatly related to the larger moisture transport toward the western part of the Baiu front than toward the eastern part. On the other hand, the rainfall characteristics around the front in the eastern Japan tend to be largely influenced by the cool Okhotsk air mass with rather stable stratification. Furthermore, their year-to-year, intraseasonal and short-period variations including the diversity of the “heavy rainfall types” are also very large.

The extreme events in association with the Baiu/Meiyu activity are greatly reflected by the above variability of the frontal activity. Inversely, it would be also important viewpoint that detailed examination of some extreme events could lead to the better understanding of the “dynamic climatological features” of the Baiu/Meiyu system itself.

In such concept, the present study will examine the frontal-scale rainfall features and the atmospheric conditions for the extremely heavy rainfall event around the Baiu front in western to central Japan during 5-7 July 2018. Although it is the common feature for the Baiu frontal rainfall heavy in western Japan that the frequent appearance of the meso-scale intense rain bands results in the huge total rainfall amount there, it is noted that the extremely large total rainfall area was distributed much more widely up to the central Japan with also considerable contribution of the long-persistent “not-so-intense rain” there, as often found in the heavy rainfall in the eastern Japan. Our analyses of the atmospheric fields suggest that this extreme event seems to be characterized by the strong mixture both of the large-scale factors for activating the “western Japan Baiu” and the “eastern Japan Baiu”.

As for the precipitation analyses, the 10-minute precipitation data at many meteorological stations in the Japan Islands area were used to discuss on the frontal-scale “rainfall characteristics” as well as the total rainfall amounts.

How to cite: Kato, K., Matsumoto, K., Yamatogi, T., and Miyake, C.: On the Baiu frontal-scale rainfall characteristics and atmospheric conditions in the extremely heavy rainfall event around western Japan during 5-7 July 2018 with attention to the synoptic climatological viewpoint, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3083, https://doi.org/10.5194/egusphere-egu2020-3083, 2020.

In the present study, a unique approach is applied to investigate the life cycle properties of the precipitation combining the satellite-based information. Data from Global Precipitation Measurement Dual Precipitation Radar (GPM-DPR) and brightness temperature (BT) form the GOES satellite. First, we used the GPM-DPR data to identify the precipitating cloud systems (PCSs) and then 9 (± 4 hours) hours of GOES BT data to identify the life phases for a particular PCSs e.g., a developing stage, a mature stage, or a dissipating stage. The case study of PCS related to different phases of the PCSs shows that PCSs consist of different systematic properties including the area of convective-stratiform precipitation, the convective rain rate and the storm-top height. The developing stage PCSs have the highest convective precipitation fraction (~26%) with highest near surface rain rate (RR, 4.97 mm h-1), whereas the dissipating stage PCSs have the largest precipitation area (11489 km2) with least near surface convective RR (~4.11 mm h-1). The vertical structure of precipitation and raindrop size distribution (DSD parameters) show the different characteristics above and below the freezing level and related with the different microphysical processes during different stages and related with the convective to stratiform area fraction and water vapour. The developing stage PCSs have the largest but sparse, droplets in convective precipitation, whereas the mature stage has the largest droplets below in the freezing level for all the vertical rainy profiles. The developing stage PCSs have the highest concentration of least sized of hydrometeors. Also, north-eastern continent of SA has higher near surface RR with higher sized of hydrometeors and even higher in developing stage PCSs. Our analysis indicates that the different microphysical properties for the PCSs in different phases are related to cloud and ice water path upward motion and related to the orographic influence.

How to cite: Kumar, S., Silva, Y., Del Castillo, C., Flores Rojas, J. L., S. Alveraz, A. M., and Castro, D. M.: Precipitation structure during the life cycle of cloud systems over Peru using satellite based observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5937, https://doi.org/10.5194/egusphere-egu2020-5937, 2020.

The distribution of precipitation on the Tibetan Plateau (TiP) is not yet understood due to various factors. Satellite-based precipitation retrieval can provide comprehensive information in a high spatial-temporal resolution. The aim of this feasibility study is to retrieve precipitation rates over High Asia using multi-spectral data from the two geostationary (GEO) satellites Elektro-L2 and Insat-3D in a 30 minutes and 4 km resolution. The variety of spectral bands from both satellites provides an insight into the cloud properties which are associated with precipitation. In the first step, the precipitation area is delineated, and in a second step, the rates are retrieved. To this end, we use a machine learning approach (Random Forest, RF) and a precipitation product of the Global Precipitation Measurement Mission (GPM IMERG) as a reference. From this product, we use the best quality gauge calibrated microwave (MW) precipitation estimates. We validate our results with independent gauge calibrated MW precipitation. To improve the RF models, we tested various optimization schemes. The results of this study will provide information about the precipitation processes in High Asia.

How to cite: Kolbe, C., Thies, B., Turini, N., and Bendix, J.: Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1402, https://doi.org/10.5194/egusphere-egu2020-1402, 2020.

The WegenerNet Feldbach Region is a unique weather and climate observation facility
comprising 155 meteorological stations measuring temperature, humidity, precipitation,
and other parameters, in a tightly spaced grid within a core area of 22 km × 16 km
centered near the city of Feldbach (46.93°N, 15.90°E).
With its stations every about two square-km (area of about 300 square-km in total),
and each station with 5-min time sampling, the network provides regular measurements
since January 2007. In 2020, the station network will be expanded by three major
new components, converting it from a 2D ground station network into a 3D open-air
laboratory for weather and climate research at very high resolution.
The following new observing components will start operations by spring 2020:

1. A polarimetric X-band Doppler weather radar for studying precipitation parame-
   ters in the troposphere above the ground network, such as rain rate, hydrometeor
   classification, Doppler velocity, and approximate drop size and number. It can
   provide 3D volume data (at about 1 km × 1 km horizontal and 500 m vertical res-
   olution, and 5-min time sampling) for moderate to strong precipitation. Together
   with the dense ground network this allows detailed studies of heavy precipitation
   events at high accuracy.
2. An azimuth-steerable microwave/IR radiometer for vertical profiling of temperature,
   humidity, and cloud liquid water in the troposphere (with 200 m to 1 km vertical
   resolution, and 5-min time sampling), also capable of measuring integrated water
   vapor (IWV) along line-of-sight paths towards Global Navigation Satellite System
   (GNSS) satellites.
3. A water vapor mapping high-resolution GNSS station network, named GNSS StarNet,
   comprising six ground stations, spatially forming two star-shaped subnets (one
   with ∼10 km interstation distance, and one embedded with ∼5 km distance), for
   providing slant IWV, vertical IWV, and precipitable water, among other parame-
   ters, at 5-min time sampling.

We will present a detailed overview of the new components, their location, specifica-
tion, and output data products.

How to cite: Fuchsberger, J., Kirchengast, G., and Bichler, C.: The WegenerNet 3D weather and climate research facility: A unique open-air laboratory for high-resolution precipitation studies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1417, https://doi.org/10.5194/egusphere-egu2020-1417, 2020.

This poster provides the design, content and purpose of the
Global Precipitation Measurement (GPM) gridded text
products. Gridded text products at the same time and space resolution are
available from the start of the TRMM period in January 1998 through the
current GPM data collection period. The poster provides an example of the
use of this data product by examining the structure of the Indian monsoon as
well as examining the monsoon during El Nino and La Nina periods. It will
also look at diurnal precipitation during the Indian monsoon season. As
part of the examination of the Indian monsoon using the gridded text
product, the poster demonstrates the ease of integration with other data.
In this case, Sea Surface Temperature (SST) data that is relevant to the Indian monsoon is examined
side-by-side with the precipitation data. It further demonstrates the ease
of aggregating the daily gridded data across many years while still
retaining the hourly structure that enables diurnal studies. The GPM
gridded text product is currently the only level 3 GPM product which can
be aggregated in this way. The representation of data in ASCII format
allows potential users to concentrate on the scientific analysis rather
than the physical format of the data. In summary, the poster provides an
overview that uses examples to demonstrate the efficacy of this unique GPM
data product.

How to cite: Stocker, E. F., Kelley, O., and West, J.: An Analysis and Example of Use of the GPM Gridded Text Products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1674, https://doi.org/10.5194/egusphere-egu2020-1674, 2020.

The operational use of observation products of rainfall for forecasting/nowcasting purposes is nowadays wide spread in several regions in the world. While the applications for such data are numerous (flood early warning systems, agriculture, urban flood, etc.) the dealing with the uncertainty of the data when they come from different sources is still an open question. In fact, due to the extreme spatial variability of the rainfall fields also when limited (but intense) rain events occur, even a quite dense ground raingauges network can be not sufficient to effectively describe the precipitation. Thus, there is the need to employ multi-sources observation systems by exploiting, when available, meteo-radar and satellite products that provide spatially continuous maps, without neglecting their limits. With specific reference to the satellite products, there are several issues about the accuracy of the reconstruction of the actual rainfall fields in terms of intensity, timing and even presence of rainfall. In this work the EUMETSAT operational rainfall product based on MSG (Meteosat Second Generation) is evaluated by comparison with the observed time series of the ground network of raingauges in the Italy territory. The focus of the comparison is to investigate on the properties of the MSG product error, in particular on how it varies  with the spatial and temporal scales of aggregation, in different regions and different seasonal periods. The analysis was conducted on the whole Italian territory, in the period 2009-2013.

How to cite: Campo, L.: Error structure of MSG rainfall operational product in Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1748, https://doi.org/10.5194/egusphere-egu2020-1748, 2020.

Abstract: This study investigates the character and complicated atmospheric influence factors of snow cover on a snowstorm event associated with Jianghuai cycle occurred from 21 to 22 February 2017 by automatic station, intensive and routine observation data. The results are as follows: (1) Special structure leads to different distribution  of snowfall and snow cover from south to north in Shandong province.(2) Snow depth rises to maximum when snow ends with timeliness. The maximum is not sure to continue at 8'clock next day.(3) Snow depth is influenced by precipitation type, snowfall, Snowfall intensity, air temperature, ground temperature and wind speed. Sleet can produce snow cover with 1cm before it converts to snow, or quantitative snow cover won't form. Snow-to-liquid ratio has great difference in various stations, and the average with 0.5cm·mm-1 in Shandong province is lower than in all country. Strong snowfall and snowfall intensive are favorable to form deep snow cover without melt snowfall, especially for air temperature and ground temperature higher than 0℃. More lower air temperature and ground temperature will benefit snow cover. The value of ground temperature threshold is about 0℃ when snow cover begin to form. The common character is that ground temperature drops quickly before visible snow cover and rises stably after 1 to 2 hours . Air temperature is usually below 0℃ when snow cover forms, or major snowfall will melt. Small wind speed is good for snow cover.
Key words: Jianghuai cycle, snowstorm, snow cover, influence factor

How to cite: Yang, C. F.: A Case Study of Snowstorm Associated with Jianghuai Cycle on Atmospheric Influence Factors and Character of Snow Cover, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1930, https://doi.org/10.5194/egusphere-egu2020-1930, 2020.

When studying the tipping bucket rain gauge (TBR), it is rather difficult to make an objective and sophisticated measurement of the duration of bucket rotation. From the perspective of digital photographic technology, however, the problem can be easily solved. The primary interest of this research has been to use digital photographic technology to study the TBR under laboratory conditions. In this study, the interframe difference algorithm and a camera recording device were used. Based on three types of JDZ TBRs, the time variation characteristics of bucket rotation were obtained. The time from the beginning of a tip to the time that the bucket is horizontal (T₁) and the time for a complete tip (T₂) were analyzed in detail. The results showed that T₁ and T₂ were functions of rainfall intensity, and T₁, T₂ decrease as the rain intensity increases significantly (P<0.001). Moreover, excellent evidence shows that the averages of T₁ and T₂ were positively correlated with bucket mass. It took more time for the bucket to tip as the mass of the bucket increased. Furthermore, the error of each TBR was calculated by the new proposed error calculation formula, and the new method was compared with the traditional method. The results from the two methods were very close, which demonstrates the correctness and feasibility of the new formula. However, the traditional calibration cannot acquire the variation characteristics of the tipping time, but the proposed approach can achieve this.

How to cite: Liao, M., Liu, J., and Liao, A.: Investigation of tipping bucket rain gauges using digital photographic technology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1931, https://doi.org/10.5194/egusphere-egu2020-1931, 2020.

Due to the importance of precipitation as a climatic and meteorological variable, it is paramount to detect the relationships between teleconnections and precipitation at different temporal and spatial scale. In fact, large-scale systems can i) influence precipitation directly, ii) establish a favourable environment to deep moist convection, and thus extreme precipitation, but also iii) help triggering dry conditions and drought.

In this study, developed within the INDECIS EU project, the teleconnection influence on precipitation in the Calabria region has been evaluated over the 1981-2010 time period, by means of a database of 79 rain gauge stations and seven teleconnections indices. Calabria, the southernmost region of peninsular Italy, was chosen as a valuable test bed mainly because it is located in the centre of the Mediterranean region, which constitutes a hot spot for climate change. Moreover, Calabria has a high-density, long-time network of precipitation gauges, recently validated and homogenized.

Statistical relationships between teleconnection indices and precipitation are often developed through well-known correlation analyses techniques, e.g. Pearson, Spearman and Kendall, where a teleconnection index is compared to cumulated precipitation values. In this study, three types of correlation analysis were performed: i) seasonal indices vs seasonal cumulated precipitation; ii) three-month indices vs monthly cumulated precipitation; iii) monthly indices vs monthly cumulated precipitation. These analyses have been performed in five Rainfall Zones (RZs) of the study area, characterised by different climatic conditions: the North-Eastern Zone (I1), the Central-Eastern Zone (I2) and the South-Eastern Zone (I3) on the Ionian side of Calabria and the North-Western Zone (T1) and the South-Western Zone (T2) on the Tyrrhenian part.

Results showed that the Mediterranean Oscillation and the North Atlantic Oscillation are the most important large-scale contributors to the precipitation regime of Calabria. Moreover, seasonal Eastern Atlantic pattern influenced seasonal precipitation in the RZs I1 and T1; three-monthly East Atlantic/Western Russian pattern influenced monthly precipitation in the RZs I2 and T1; three-monthly Western Mediterranean Oscillation influenced monthly precipitation in the RZs I3 and T1; while three-monthly El Nino-Southern Oscillation influenced monthly precipitation in the RZ T2.

Investigating changes in the response of local precipitation and teleconnections throughout the 1951-2010 and 1951-1980 time periods, a change in precipitation response to teleconnection patterns emerged, i.e., in the impact that the Mediterranean Oscillation has on the East coast precipitation (RZs I1-I3), a possible result of natural variation or climate change. In addition, these results have been compared to those obtained with the classical correlation analyses between teleconnection indices and single-station precipitation.

The approach developed for this study is a general method that, in principle, can be reproduced for any variable for any region and for every teleconnection.

Acknowledgments:

The Project INDECIS is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

How to cite: Caroletti, G. N., Coscarelli, R., and Caloiero, T.: A sub-regional approach for the analysis of atmospheric teleconnection influence on precipitation in Calabria (southern Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3078, https://doi.org/10.5194/egusphere-egu2020-3078, 2020.

Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE). Utilizing the C-band polarimetric (CPOL) radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (Z_H), the specific attenuation (A_H), the specific differential phase (K_DP), and their corresponding double-parameters that further integrate the differential reflectivity (Z_DR), namely R(Z_H, Z_DR), R(K_DP, Z_DR) and R(A_H, Z_DR), are investigated for an extreme precipitation event occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution (DSD) data observed by two disdrometers. The results show that (i) Each radar QPE estimator has its own advantages and disadvantages depending on the specific rainfall patterns, and it can outperform other estimators at a certain period of time; (ii) although R(A_H, Z_DR) underestimates in the light rain pattern, it performs best of all radar QPE estimators according to statistical scores; (iii) Both the optimal radar rainfall relationship and the consistency between radar measurements aloft and surface observations are required to obtain accurate rainfall estimates close to the ground. The contamination of melting solid hydrometeors on A_H and/or K_DP may make them less effective than Z_H. In addition, appropriate α coefficient can eliminate the melting impact on the A_H-based rainfall estimator.

How to cite: Gou, Y., Chen, H., and Zhou, J.: Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3252, https://doi.org/10.5194/egusphere-egu2020-3252, 2020.

Because of low precipitation and its severe fluctuations in Iran, understanding the dynamics of large scale climate modes and probability of annual and intraseasonal precipitation variation is essential for water management. This study investigates the characteristics of the combined effects of Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO) on precipitation over Iran. Daily precipitation and atmospheric data (relative humidity and vertical velocity) were analysed over Iran during wet season (October to May) for the period 1961 to 2018. The results indicated that: 1) Distinct difference can be observed in spatial distribution of the probability of daily precipitation above upper tercile for MJO phases, phase 1 and 2 wetter while 4 and 5 are drier. The relative humidity is higher in phases (1-3 and 7-8) and lower in phases (4-6). The vertical velocity shows upward (downword) motion in phases 1-2 and 7-8 (3-6). 2) Response of rainy season precipitation over Iran to MJO is more affected by the large-scale atmospheric variation associated with negative NAO as compared to positive NAO. In the negative NAO, the MJO increase (decrease) the probability of upper tercile precipitation 1.2 (0.7) times in phases 2-3 (4-6) and significant tests show a significantly large response for west and North west of Iran. In contrast of positive NAO, the relative humidity and vertical velocity is more affected by negative NAO state. The more (less) humidity and upward (downward) motions increase (decrease) precipitation in phases 2-3 (4-6).

How to cite: Pourasghar, F., Babaiean, I., and Ghaemi, H.: The relationship between intraseasonal precipitation of Iran and combined effects of MJO and NAO, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3939, https://doi.org/10.5194/egusphere-egu2020-3939, 2020.

The diameter, mass and density of individual falling snowflakes is being measured automatically in Salt Lake City, Utah using a new device called the  Differential Emissivity Imaging Disdrometer (DEID). Hydrometeor properties are obtained from the DEID using a temperature-controlled hotplate to melt and evaporate hydrometeors and a thermal camera based upon the large difference in thermal emissivity between water and the aluminum hotplate. The density of each particle is calculated from the initial effective diameter imaged by the thermal camera and the individual particle mass, by assuming conservation of energy for heat transfer from the plate to the melted droplet and measuring the time taken for evaporation. Simultaneously recorded Multi-Angle Snowflake Camera (MASC) imagery provides hydrometeor types. These data are revealing detailed structures of snowfall density suited for avalanche studies, atmospheric precipitation rate, snow water equivalent and visibility, and size, and the mass and density distributions of individual particles. Results are generally consistent with past studies by e.g. Marshall and Palmer, Marshall and Gunn and Locatelli and Hobbs. However, order one million particles can be collected in a single storm cycle, so the range of particle collected and the statistical validity of the analyses is providing new insights into the nature of frozen precipitation.

How to cite: Rees, K., Garrett, T., Singh, D., Pardyjak, E., and Reaburn, A.: A new particle–by –particle hot plate technique for measurement of precipitation rate, snow density and visibility, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6148, https://doi.org/10.5194/egusphere-egu2020-6148, 2020.

The scale of precipitation systems can provide important information to acquire a better understanding of formation mechanism and environmental effects of precipitation as well as model promotion. However, the global geographical distribution of precipitation system scale remains poorly known from previous studies. This study uses the latest Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data to get global patterns of precipitation system scale by grouping the contiguous rainy gridboxes during 2015-2018. Our results show that the large precipitation systems (>10³ km) occur more frequently over ocean and the midlatitude land areas with low precipitation amount such as Siberia as well as the western and central parts of North America. The most apparent seasonal variation of precipitation system scale occurs over midlatitude ocean along with the northern and southern coast of South America. Most regions of the world have the highest peak in the late afternoon at around 17:00 local time (LT). In a statistical average, the relationships between scale and other precipitation properties including amount, frequency, intensity and duration all seem to be positive. The strongest associations of scale with amount, frequency, intensity and duration all occur over tropics and ocean with the highest correlation coefficient greater than 0.8.

How to cite: Zhang, Y. and Wang, K.: The quasi-global geographical distribution of precipitation system scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5477, https://doi.org/10.5194/egusphere-egu2020-5477, 2020.

Accurate rainfall information in high spatio-temporal resolution is important for water resource management, particularly in water-scarce remote areas which are characterized by a coarse network of operational precipitation gauge stations. For such regions, satellite-based rainfall products potentially represent a source for reliable and area-wide data on rainfall. The poster presents a new satellite-based precipitation algorithm for semi-arid regions in Ecuador with the elevation range between 12 to 5700 a.s.l. The algorithm relies on the combination of  precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite 16 (GOES-16). The algorithm is developed to (i) classify the rainfall area and then (ii) to assign the rainfall rate. For the period between 19.04.2017 to 19.04.2018 the brightness temperature derived from GOES-16 IR channels and ancillary geo-information are trained with microwave only rainfall information of the half-hourly IMERG-V06 product using the machine learning algorithm random forest. The validation is done against independent microwave-only IMERG-V06 rainfall data not used for model training and available gauge stations. The validation results show overall very good accuracy of the new rainfall retrieval technique in this case study, mostly in comparison with the GPM IMERG IR-only rainfall product. The product offers the potential for high spatio-temporal (2 km, 15 min) rainfall resolution in near real-time for Ecuador.

How to cite: Turini, N., Thies, B., Rollenbeck, R., Fries, A., Pucha-Cofrep, F., Orellana Alvear, J., Horna, N., Célleri, R., and Bendix, J.: Retrieving high resolution rainfall data for Ecuador using GOES-16 and IMERG data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6594, https://doi.org/10.5194/egusphere-egu2020-6594, 2020.

In this contribution, we investigate hydrometeors and their distribution in thunderclouds. We classify 5 kinds of hydrometeors using data of a Ka-band cloud profiler (35 GHz) situated at the weather station Milešovka in Central Europe. The classification of hydrometeors is based on calculated vertical air velocity, terminal velocity of a target, minimum and maximum terminal velocity of hydrometeor classes, and Linear Depolarization Ratio within three temperature intervals. We performed the classification for convective events that were observed at the station in 2018 and 2019 and were related to lightning in the vicinity of the station.

Results suggest that there is a link between lightning flashes observed close to the weather station (based on EUCLID data) and the presence of graupel, ice, snow, and hail. These are the hydrometeors (graupel and ice in particular) that are considered to play major role in thundercloud electrification by the collision of hydrometeors.

How to cite: Minářová, J. and Sokol, Z.: Hydrometeor classification in convective clouds using cloud profiler data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5025, https://doi.org/10.5194/egusphere-egu2020-5025, 2020.

As global warming has progressed, precipitation patterns over arid Northwest China have undergone significant change. In this study, changes in summer (JJA) precipitation over the eastern part of Northwest China (ENWC) from 1980 to 2014 were investigated using the China gridded monthly precipitation dataset (CN05.1). The results showed that summer precipitation over the ENWC experienced a decadal wet-to-dry shift in 1998. Westerlies played an important role in the upper atmospheric levels in terms of water vapor transport; the decadal variations in summer precipitation were principally controlled by the water vapor input from the ENWC's western boundary. In addition, the decadal variations in summer precipitation in the ENWC appear to be associated with a meridional teleconnection around 110°E and a zonal pattern over 45–60°N in the lower troposphere. These two teleconnections led to cyclonic anomalies in the ENWC and enhanced water vapor transport into the ENWC, resulting in above-normal precipitation during the 1989–1998 decadal period. Further, the warmer (colder) sea surface temperatures (SSTs) observed in the tropical Eastern Pacific correspond to the southward (northward) displacement of the Asian jet stream and a negative (positive) phase of the Silk Road pattern, resulting in a wet (dry) ENWC. Moreover, the SST anomalies in the North Atlantic and Northwest Pacific may affect summer precipitation over the ENWC via a zonal teleconnection in the middle troposphere. Details about the results will be presented in the conference.

How to cite: Shang, S., Zhu, G., Li, R., Xu, J., Gu, J., Chen, H., Liu, X., and Han, T.: Decadal change in summer precipitation over the east of Northwest China and its associations with atmospheric circulations and sea surface temperatures , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18819, https://doi.org/10.5194/egusphere-egu2020-18819, 2020.

Satellite-based remote sensing provides a unique opportunity for the estimation of global precipitation patterns. In order to use this approach, it is crucial that the uncertainty in the satellite estimations is precisely understood. The retrieval of high-latitude precipitation (especially shallow precipitation) remains challenging for satellite precipitation monitoring. This project will quantify the quality of the precipitation estimations obtained from the Global Precipitation Measurement (GPM) mission, where the focus will be on the level II and III products. Initially, the Netherlands is chosen as research area, since it has an excellent infrastructure with both in-situ and remotely sensed ground-based precipitation measurements, its flat topography, and the frequent occurrence of shallow precipitation events. The project will study the influence of precipitation type and the impact of the seasons on the accuracy of the GPM products. Hereafter, the project will focus on the physical causes behind the discrepancies between the GPM products and the ground validation, which can be used to improve the retrieval algorithms. The presentation will outline the project structure and will demonstrate the initial results.

How to cite: Bogerd, L., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Deepening our understanding of shallow precipitation measurements from space, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7511, https://doi.org/10.5194/egusphere-egu2020-7511, 2020.

Cornerstone of the meteorological and climatological science is the quality measurements of the precipitation. Large instrumentation gaps occur due to network destructions (fires, wars) or even technical limitations that dictate network reorganizations. This is a difficult to tackle issue as there are legacy networks that provide decades of valuable data, but for various reasons have been discontinued. A method to work out such problems is to include only part of the data to the analyses, or to use methods to fill the measuring gaps from nearby stations, such as interpolation techniques, regression techniques. In this work, we present and assess a method to estimate missing values in daily precipitation series based on a quantile mapping approach, originally used for bias correction of climate model output. The overall methodology is based on a three-step procedure. The first is to assess the missing values from nearby stations using inverse distance weighting interpolation method. Then, as a second step, the wet day fraction is adjusted to fit the respective fraction of the target point existing data. The third step is to adjust the biases in the probability density function of the filled values towards the target point existing data, using the Multi-segment Statistical Bias Correction methodology (MSBC- Grillakis et al., 2013). The methodology is applied to each calendar month separately. The presented methodology has the advantage of correcting the number of rainy days that is usually overestimated by conventional interpolation approaches, as well as, better reproduces large daily precipitation values. The methodology is assessed for its performance on completing the timeseries of a dense precipitation stations network, using data of a second, also dense station network for the island of Crete – Greece. Conceptual limitations of the method are discussed.

Acknowledgments: This research has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology Hellas (GSRT), under Agreement No 651.

How to cite: Grillakis, M. G., Polykretis, C., and Alexakis, D. D.: A method to fill-in discontinued daily precipitation series from nearby stations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9718, https://doi.org/10.5194/egusphere-egu2020-9718, 2020.

On February 20, 2010, Madeira island was affected by a tragic event of extreme precipitation. The event was marked by huge economical damage estimated in millions of euros, and more than 40 deaths. Before the event, there were not many studies about severe precipitation in Madeira, which were highly motivated after 2010. This work intent is to show some advancements in knowledge of heavy precipitation events (HPE) in Madeira found in the last decade. The Meso-NH model was used with a rather complete parametrization package of several physical processes occurring in the atmosphere and configured into different dimensions. In order to explore the meridional water vapour transport, the total precipitable water field was extracted from the Atmospheric Infrared Sounder (AIRS) data products. In the first set of simulations, the experiments were performed with three horizontal nested domains (9 km, 3 km, and 1 km resolution). The results for the winter 2009-2010 raised two questions about the topic. First, associated with the large scale environment, and the second one linked to orographic effects. In the first case, apart from a cyclone affecting the island, it was identified the presence of atmospheric rivers (ARs) coupled to frontal systems transporting tropical moisture toward the island. For the orographic effects, the simulations at 1km resolution showed maximums of accumulated precipitation in the highlands. Subsequently, the analysis of the precipitation in Madeira highlands over a 10-year period showed dry summers and the highest rainfall amounts in the winters, although with some significant events occurring also in autumn and spring seasons. Furthermore, it was found that tropical moisture transported through the ARs may reach the island with different intensities and orientation during the winter seasons. However, for the 10 winter periods, the ARs were not the sole factor producing HPE in Madeira. In the second set of simulations, the model was configured with a larger domain of 2.5 km resolution and an inner domain of 0.5 km resolution. All the significant events in autumn 2012 were simulated confirming the orographic effect in the accumulated precipitation. The most interesting result found was the occurrence of maximums values in different regions over the island. For example, over the highlands in the central peaks and southern/northern slopes, or in the coastal plain at lowlands. From the simulations it was possible to explain the causes for the distinct rainfall patterns, and the atmospheric environments associated. The variations in the configuration of the ambient flow, jointly with the orographic forcing may produce convection in distinct regions of the island, resulting in different rainfall patterns. Ten years later, the advances in the understanding of significant precipitation in the Madeira is evident. The results show how different events may occur, since the formation or enhancement of the precipitation over the island is totally dependent on the geographic aspects and atmospheric conditions associated with each precipitating event.

How to cite: Salgado, R., Couto, F. T., and Costa, M. J.: Advances in knowledge 10 years after the torrential rains in Madeira Island (Portugal), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10932, https://doi.org/10.5194/egusphere-egu2020-10932, 2020.

Evaluations of satellite precipitation estimates have been routinely conducted, often in individual gridbox. However, real precipitation is organized as precipitation system in space with a certain extent and structure. Evaluation from the perspective of precipitation system to understand the relationship between satellite errors with the spatial extent of precipitation system and relative location in precipitation system may help to the better knowledge of satellite error sources but has rare been concerned. To address this issue, the Integrated Multi-satellitE Retrievals for GPM (IMERG) V05B final run half-hourly product is evaluated in this study with hourly rain gauge data collected at approximately 50,000 stations in China. We first identify the precipitation system in IMERG and make comparison in gridboxes with gauge observations as a function of gridboxes’ distance to the boundaries of system and the system sizes to investigate their relationships. Our results show that the false alarm proportions generally decrease as the increase of precipitation system sizes, while it is opposite for the miss proportions. Both the miss and false alarm proportion evidently decrease with the longer distance from the boundaries. Over 90% false alarms occur within the distance of 10% of the square root of precipitation system sizes from the boundaries, while 90% misses locate within the distance of 20% the square root of system sizes. The difference between the false alarm proportion inside the systems and miss proportion outside the systems in accordant distances from the boundaries indicate the generally overestimation of IMERG precipitation system sizes, but much severer for small systems than large systems. For the hit bias, IMERG generally underestimates in small precipitation systems but have regional-dependent sign of bias for larger systems. Accordantly, IMERG underestimates the hit precipitation rates for all distances from the boundaries, but the situation is more complex for larger precipitation systems, with an overestimation for about 0-50 km but underestimation for about 50-200km and again overestimation for over about 200km, indicating the evident location-dependent errors in precipitation system of IMERG.

How to cite: Li, R., Wang, K., and Qi, D.: Dependence of GPM precipitation product performance on the spatial extent of precipitation system and relative location in precipitation system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11060, https://doi.org/10.5194/egusphere-egu2020-11060, 2020.

The goal of this study is to create a high-resolution grid of precipitation indices for the wider Greek region using real data from meteorological stations for the 1980-2010 period. Under the risk of increased extreme events caused by climate change, it is important to be able to have a high-resolution gridded extreme precipitation indices in order to overcome the lack of density of observations in both time and space. The development of such a grid can be used to validate model outputs and inform decision makers to better mitigate the damage from extreme precipitation.

The first step of the analysis is to calculate the extreme precipitation indices based on daily observations derived from more than 100 meteorological stations covering a wide range of altitudes and spatial climate patterns existing in Greece. Thereafter, the extreme indices will be multilinearly downscaled to a 12-meter resolution grid. The geophysical parameters used in the downscaling procedure consists of altitude, latitude, longitude, slope, aspect, solar irradiance and Euclidian distance from the water bodies. The altitude information came from the highly accurate 12-meter resolution TanDEM-X Elevation Model, which is a product generated from the TerraSAR-X satellite mission data. The resulting high-resolution patterns will give insight of the spatial and temporal variability of extreme precipitation, over the complex terrain of the wider Greek region.

How to cite: Nastos, P. T., Ntagkounakis, G. E., and Vassilakis, E.: High-resolution gridded extreme precipitation indices for the wider Greek Region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17684, https://doi.org/10.5194/egusphere-egu2020-17684, 2020.

The Antarctic Ice Sheet plays a major role in regional and global climate variability and represents, probably, the most critical factor of future sea-level rise. Snow and solid precipitation more broadly have been recognized as primary mass input for ice sheet. However, despite its fundamental role in the surface mass balance estimation, precipitation over Polar region and in the Antarctica particularly, remains largely unknown, being not well assessed by numerical weather/climate models, by ground observations and satellite measurements as well. More accurate estimations of precipitation in the Antarctic continent are desirable not only in understanding the behavior of the Antarctic Ice Sheet, but also in validating global climate and numerical weather prediction models and also in order to constrain measurements from space during validation/calibration satellite campaigns.

Recently, several observatories in Antarctica have been equipped with equipment for cloud and precipitation measurements, such as the two Italian stations “Mario Zucchelli”, Terra Nova Bay, and Concordia, in the Antarctic Plateau. At “Mario Zucchelli”, instrumentation includes 24-GHz vertical pointing radar Micro Rain Radar (MRR) and optical disdrometer. The synergetic use of such set of instruments allows for characterizing and quantifying precipitation, even if quantitative estimate of precipitation from radar is extremely demanding, especially in snowfall, because of variability microphysical features of hydrometeors.

Usually precipitation estimation methods with weather radar are based on relationships between radar equivalent reflectivity factor (Ze) and liquid equivalent snowfall rate (SR). Several relationships are reported in literature, derived from comparison between radar and ground sensors but very few are suitable for the Antarctic continent and none also considers the microphysical characterization of hydrometeors.

This work shows quantitative estimate of the Antarctic precipitation for several snow episodes at the Mario Zucchelli station using specific ZE-SR relationships also taking into account the snowfall classification according to dominating hydrometeor type (e.g. pristine, aggregate, dendrite, plate). Microphysical properties of precipitation are inferred by comparing radar measurements with simulations obtained from disdrometer measurements in terms of reflectivity factor. Specifically, the Ze directly derived by radar has been compared with the Ze calculated by disdrometer observations coupling particle size distributions and NASA database of hydrometeor backscattering values based on the Discrete Dipole Approximation. More challenging are estimations at Concordia, where ice particles have very small sizes and are hardly detectable by laser disdrometer, and where MRR lacks of adequate sensitivity.

How to cite: Bracci, A., Roberto, N., Baldini, L., Montopoli, M., Adirosi, E., Gorgucci, E., Scarchilli, C., Grigioni, P., Ciardini, V., Di Natale, G., Facheris, L., Levizzani, V., and Porcù, F.: Quantitative precipitation estimation in Antarctica using different ZE-SR relationships based on snowfall classification combining ground observations by radar and disdrometer, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22460, https://doi.org/10.5194/egusphere-egu2020-22460, 2020.

Assessing the accuracy of precipitation sensors can prove very challenging due to the lack of a universal test standard, stemming from difficulties in creating a controlled test scenario. We propose a refined method of testing that is highly reproducible and can be applied to any precipitation sensor regardless of sensing technology.

It is widely understood that two identical disdrometers mounted close together in a real rain event are not likely to report the same precipitation measurements due to the small scale spatial variation of rain. This makes it difficult to draw comparisons between sensors of the same type and even more difficult to compare rain sensors that have different sensing areas and use different sensing technologies. It is therefore desirable to simulate rainfall in the laboratory that is representative of real world conditions but this presents its own set of challenges, primarily in creating rain drops that travel at terminal velocity. This test method significantly reduces the impact of this issue.

This is particularly important for sensors such as optical, acoustic, radar or impact, where the calculations used to obtain rainfall accumulation and drop size distribution assume that the droplets are at terminal velocity. Even for sensors such as capacitive rain gauges and tipping buckets, where the velocity of fall is not directly related to the measurements, more valid conclusions can be drawn about the sensor’s ability to measure precipitation when the droplets imitate real rainfall as closely as possible.

Here, the development of a drip rig capable of creating raindrops of a controlled size is documented. The drip rig can be mounted at a known height and used to test a variety of different precipitation sensors. However, due to height restrictions in the laboratory, it is not possible to get larger raindrops to terminal velocity. Mounted at a height of 7.4m, drops above 2 mm in diameter do not reach 99% terminal velocity, and drops above 3 mm do not reach 95%. For this reason, corrections must be applied to the calculations. It is therefore essential to have an understanding of the change in fall velocity of a water droplet with fall distance.

This work documents the equations used to calculate drop velocity with fall distance for different drop masses. Temperature, humidity and air pressure define air density, which has a significant impact on the velocity of a falling water droplet. The effect of each of these environmental factors has been investigated in order to allow for further corrections. Performing these corrections greatly improves the validity and repeatability of the tests carried out on precipitation sensors.

How to cite: Frame, G. and Spencer, E.: The development of a laboratory drip rig for performing reproducible comparisons on different precipitation sensors by correcting for environmental conditions and rig set up, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18046, https://doi.org/10.5194/egusphere-egu2020-18046, 2020.

An increasing number of satellite-based rainfall estimates, with ever finer resolution, are becoming available. They are particularly valuable in regions with sparse radar and gauge networks. For example, in most of sub-Saharan Africa, the gauge network is not dense enough to represent the high variability of the rainfall during the monsoon season. However, satellite-based estimates can be subject to errors in position and/or timing of the rainfall events, in addition to errors in the intensity.
Many satellite-based estimates use gauge measurements for bias correction. Bias correction methods focus on the intensity errors, and do not correct the position error explicitly. We propose to gauge-adjust the satellite-based estimates with respect to the position and time. We investigate two approaches: spatial and temporal warping. The first one is based on a spatial mapping and correct the spatial position while keeping the time constant. The second uses a temporal mapping and keeps the spatial domain unchanged. The mappings are derived through a fully automatic registration method. That is, only the gauge and satellite-based estimates are needed as inputs. There is no need to manually predefine the rain features.
The spatial and temporal approaches are both applied to a rainfall event during the monsoon season in southern Ghana. The Trans-African Hydro-Meteorological Observatory (TAHMO) gauge network is used to gauge-adjust the IMERG-Late (Integrated Multi-Satellite Retrievals for GPM) satellite-based estimates. The two approaches are evaluated with respect to the timing, the location and the intensity of the rainfall event.

How to cite: Le Coz, C., Heemink, A., Verlaan, M., ten Veldhuis, M., and van de Giesen, N.: Correcting position error in rainfall estimates using temporal and spatial warping, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18417, https://doi.org/10.5194/egusphere-egu2020-18417, 2020.

Within the Copernicus Climate Change Service (C3S), the Climate Data Store (CDS) built by ECMWF will provide open and free access to global and regional products of Essential Climate Variables (ECV) based on satellite observations spanning several decades, amongst other things. Given its significance in the Earth system and particularly for human life, the ECV precipitation will be of major interest for users of the CDS.

C3S strives to include as many established, high-quality data sets as possible in the CDS. However, it also intends to offer new products dedicated for first-hand publication in the CDS. One of these products is a climate data record based on merging satellite observations of daily and monthly precipitation by both passive microwave (MW) sounders (AMSU-B/MHS) and imagers (SSMI/SSMIS) on a 1°x1° spatial grid in order to improve spatiotemporal satellite coverage of the globe.

The MW sounder observations will be obtained using, as input data, the FIDUCEO Fundamental Climate data Record (FCDR) for AMSU-B/MHS in a new global algorithm developed specifically for the project based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR; Sanò et al., 2015), adapted for climate applications (PNPR-CLIM). The algorithm consists of two Artificial Neural Network-based modules, one for precipitation detection, and one for precipitation rate estimate, trained on a global observational database built from Global Precipitation Measurement-Core Observatory (GPM-CO) measurements. The MW imager observations by SSM/I and SSMIS will be adopted from the Hamburg Ocean Atmosphere Fluxes and Parameters from Satellite data (HOAPS; Andersson et al., 2017), based on the CM SAF SSM/I and SSMIS FCDR (Fennig et al., 2017). The Level 2 precipitation rate estimates from MW sounders and imagers are combined through a newly developed merging module to obtain Level 3 daily and monthly precipitation and generate the 18-year precipitation CDR (2000-2017).

Here, we present the status of the Level 2 product’s development. We carry out a Level-2 comparison and present first results of the merged Level-3 precipitation fields. Based on this, we assess the product’s expected plausibility, coverage, and the added value of merging the MW sounder and imager observations.

References

Anderssonet al., 2017, DOI:10.5676/EUM_SAF_CM/HOAPS/V002

Fennig, et al., 2017, DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003

Sanò, P., et al., 2015, DOI: 10.5194/amt-8-837-2015

How to cite: Panegrossi, G., Sanò, P., Bagaglini, L., Casella, D., Cattani, E., Konrad, H., Niedorf, A., Schröder, M., Mikalsen, A. C., and Hollmann, R.: Development of a microwave-based precipitation climate data record for the Copernicus Climate Change Service, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18636, https://doi.org/10.5194/egusphere-egu2020-18636, 2020.

Optical disdrometers are among the non-catching type instruments used to measure liquid and solid precipitation. The increasing use of such instruments in operational observations is due to their capability to provide additional information than the precipitation rate alone, like e.g. the particle size distribution and the fall velocity of hydrometeors. Furthermore, they are well suited to operate in unattended, automatic weather stations. Having no collector to catch the approaching hydrometeors, their outer shape strongly depends on the measuring principle exploited. The impact of wind on the measurement is therefore different from the typical undercatch that is expected from more traditional catching type precipitation gauges. In general, they are not axisymmetric and base the identification and classification of hydrometeors on the coupling of particle size and fall velocity characteristics, which can be affected by the wind and by the airflow deformation and turbulence produced by their bluff-body aerodynamic response. The focus of this work is the Thies Laser Precipitation Monitor (LPM), which uses an optical sensor to detect the obstruction of an infrared laser beam caused by the crossing hydrometeors. The reduction in the sensor output voltage is proportional to the drop dimension, while the duration of the reduction is proportional to the drop falling speed. This instrument presents a very complex and not axisymmetric outer shape that makes it difficult to qualitatively predict the flow pattern and requires to consider multiple wind directions and wind speeds. The airflow field was obtained with a Computational Fluid Dynamics (CFD) approach, by numerically solving the Reynolds Averaged Navier-Stokes equations with the k-ω SST turbulence closure model. Results are validated through local flow velocity measurements obtained in the DICCA wind tunnel. The Thies LPM® was placed in the measuring chamber of the wind tunnel (1.7 x 1.35 x 8.8 m) on to a rotating plate and the airflow velocity was sampled at multiple positions around the instrument. The measurements were obtained using a traversing system equipped with a “Cobra” multi hole pressure probe, that provides the three velocity components of the local flow. Different orientation angles of the gauge with respect to the incoming flow direction were tested. Based on the simulations and wind tunnel tests performed, the less impacting configuration of the instrument relative to the main wind direction is obtained. The information can be useful to design effective solutions to minimise the impact of wind and turbulence on the measurements (e.g. windshields) and to derive suitable correction curves to improve the measurement accuracy. This work is funded as part of the activities of the EURAMET – Normative project “INCIPIT – Calibration and Accuracy of Non-Catching Instruments to measure liquid/solid atmospheric precipitation”.

How to cite: Stagnaro, M., Chinchella, E., Cauteruccio, A., and Lanza, L. G.: Bluff body aerodynamics of the Thies Laser Precipitation Monitor investigated using CFD and wind tunnel measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18803, https://doi.org/10.5194/egusphere-egu2020-18803, 2020.

The Friulian Alps show peculiar meteorologica and climatic features, deriving also from their geographic position between the northern Adriatic Sea to the South, the main Alpine watershed to the North (Tauern Alps) and the Carpathian belt to the East. Furthermore, there are many topoclimatic situations in relation to the geographic setting of the valleys carved between the main reliefs. This makes the Frioulian territory among the wettest in the entire Alpine region, with very abundant snowfall in relation to the moderate average altitude. Thanks to the availability of continuous and fairly homogeneously distributed historical series, a nivological characterization was carried out at the regional scale, with particular attention to the trend of the density of fresh snow, of the number of days with snow thickness higher than 30 cm and the consequent average elevation of the threshold of 100 skiable days (LAN). The ten snow fields under examination are located at elevations between 603 m. (Claut, Carnic Prealps) and 1843 m. (Rifugio Gilberti, Julian Alps); the analysed timespan goes from the winter season 1990-91 to the 2018-19. Surprising data resulted from this analysis. First of all, we noted that the volume mass (Kg /m³), which cannot be correlated with altitude, tends to a very light decrease (about 1.3 km/mc for year) in all the recording stations: this seems to be in contrast with the strong thermal increase that is occurring also on the Frioulian Alps (about 1.1°C in the same time span). Therefore, it’s very probable that in the last few years the thermal characteristics have changed, maybe together with the main origin of the air masses bringing snow in the study area. We also noted for all the stations an increase in the number of days with Hs> 30 cm: consequently, the average elevation of the limit of 100 days with natural ski possible is at about 1780 m a.s.l. and tends to decrease by about 7 meters per year (14 m in the nearby Slovenian Alps), even though it cannot be correlated with the aforementioned positive variation in temperatures and is in disagreement with the corresponding signals calculated for the northern side of the Alps.

How to cite: Fazzini, M., Cecili, A., Miccadei, E., Moro, D., and Bisci, C.: Analisys of recent snow volumic mass and elevation of the threshold of natural skiability (lan) in the frioulan alps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22114, https://doi.org/10.5194/egusphere-egu2020-22114, 2020.

Warm clouds have a huge impact on radiative forcing and also precipitation properties. Knowledge about their raindrop size distribution (RSDs) is useful in realizing rain integral parameter and in the understanding of precipitation microphysics. Unfortunately, as a result of the discontinuity of spatiotemporal observation, obtaining a detailed process that occurs in warm clouds is still challenging. In this study, we try to identify the characteristics of cloud microphysical processes in warm rain formation over Northern Taiwan. The detailed analysis is conducted by using a combination of Joss-Waldvogel Disdrometer (JWD) and Himawari-8 in North Taiwan from December 2017 to January 2018.

The preliminary result shows that different rainfall intensity can build different kinds of RSDs.  In Taiwan winter season, warm rain has a lower concentration of midsize and large raindrops as compared to mixed and cold rain. However, small raindrops are more dominant than middle and large drops for warm rain. It is found that both microphysical properties (Cloud Optical Thickness/COT, Cloud Liquid Water Path/CLWP, and Cloud Effective Radius/CER) and Gamma parameter distribution are varied as the rain rate varied. A lower rain rate (e.g., drizzle) has resulted from a wider range of cloud microphysical properties while a higher rain rate (e.g., stronger rain rate) has resulted from certain ranges of cloud microphysical properties. The Gamma parameter distribution shows more homogenous distribution as the rain rate increase.

How to cite: Matondang, C. A. and Liu, C.-Y.: Warm Cloud Microphysical Properties and their Associated Raindrop Size Distributions over Northern Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19938, https://doi.org/10.5194/egusphere-egu2020-19938, 2020.

Microphysical processes in cold precipitating clouds are not fully understood and their parametrization in atmospheric models remains challenging . In particular the lack of evaluation and validation of the microphysical parameterizations in polar regions questions the reliability of the ice sheet surface mass balance assessments. Recently, strong discrepancies have been found in the precipitation structure between simulations with different microphysical parameterizations over the Antarctic coast.

The dissimilarities between simulations seem to be due to different treatments of the riming, aggregation and sublimation processes.

Evaluating the representation of a particular microphysical process in a model is delicate, especially because it is difficult to obtain in situ estimations, even qualitative, of a given microphysical process. In this study, we developed a method to identify the regions in radar scans where either aggregation and riming, vapor deposition or sublimation are the dominant microphysical processes.

This method is based on the vertical (downward) gradients of reflectivity and differential reflectivity computed over columns extracted from range height indicator scans. Because of the expected increase in size and decrease in oblateness of the particles, aggregation and riming are identified as regions with positive gradients of reflectivity and negative gradients of differential reflectivity. Because of the expected increase in size and oblateness, vapor deposition is identified as regions with positive gradients of reflectivity and positive gradients of differential reflectivity. Because of the expected decrease in size and in concentration, snowflake sublimation, and possibly snowflake breakup, are defined as regions with negative gradients of reflectivity.

The method was employed on two frontal precipitation events, which took place during the austral summer APRES3 campaign (2015-2016) in Dumont d’Urville (DDU) station, Antarctic coast. Significant differences appear in the mean altitudinal distribution where each process takes place. Given that the radar signal extends up to 4500 m a.g.l., we could show that crystal growth dominates around 2800 m while aggregation and riming prevail around 1500 m. Sublimation mostly occurs below 900 m, concurring with previous studies stating that snowflakes preferentially sublimate in the relatively dry katabatic boundary layer.

Moreover the statistical distributions of different radar variables provides quantitative information to further characterize the microphysical processes of interest.

This method could be further used to assess the ability of atmospheric models to reproduce the correct microphysical processes at the correct locations.

How to cite: Planat, N., Gehring, J., Vignon, E., and Berne, A.: Automatic identification of the dominant microphysical processes in snowfall over the Antarctic coast using polarimetric radar observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21653, https://doi.org/10.5194/egusphere-egu2020-21653, 2020.

Since March 2014, the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) has provided satellite precipitation estimates across the globe. Using gridded surface precipitation data derived from local rain gauges as a reference, this study evaluated the performance of IMERG in depicting the spatial-temporal characteristics of precipitation variations over Taiwan at multiple (including annual, seasonal, intraseasonal, diurnal and semidiurnal) timescales. The analysis focused on the period of March 2014-February 2017. Our results show that, quantitatively, IMERG underestimated the magnitude of precipitation over most of Taiwan for all the examined timescales; spatially, the bias in variability was larger over the mountainous areas than over the plain areas; temporally, the bias in variability was larger in the warm seasons than in the cold seasons. Despite the magnitude differences, IMERG was capable of qualitatively depicting several distinct features of Taiwan precipitation changes, listed as follows: (1) a bimodal pattern, with a peak in May and another peak in September, in the annual evolution of precipitation area averaged over Taiwan; (2) a seasonal counterclockwise rotation feature, with the precipitation maximum located over northern Taiwan in the winter, over northwestern Taiwan in the spring, over southwest Taiwan in the summer and over eastern Taiwan in the autumn; (3) a 10-to-35-day intraseasonal oscillation feature, with a transition of variations from smaller amplitudes in the cold seasons to larger amplitudes in the warm seasons, occurring around mid-May (i.e., the so-called Meiyu onset in Taiwan); and (4) a roughly out-of-phase feature, with a morning precipitation maximum in the winter and an afternoon precipitation maximum in the other seasons, for the diurnal evolution of the area-averaged precipitation over Taiwan. In addition, IMERG was capable of qualitatively depicting the phase evolution of semidiurnal precipitation over Taiwan in most seasons, except for the winter season.

How to cite: Huang, W.-R., Chang, Y.-H., and Liu, P.-Y.: Assessment of IMERG precipitation over Taiwan at multiple timescales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1907, https://doi.org/10.5194/egusphere-egu2020-1907, 2020.