AS1.13
Presentations: Fri, 27 May | Room E2
A major proportion of the global precipitation falls at the tropical oceans. Nonetheless, due to the lack of in-situ precipitation measurements, studies over the ocean and so over the tropical oceans remain limited. Among others, the Integrated Multi-Satellite Retrievals for GPM (IMERG) is currently one of the best satellite estimates and has been widely applied in various research applications. However, its performance over the ocean, and specifically, over the tropical oceans is yet to be known. Thus, in this study, we quantitatively evaluate the IMERG V06 Early, Late and Final products using along-track shipboard data (OceanRain dataset) and in-situ data (buoy observations from the Global Tropical Moored Buoy Array; GTMBA) across the tropical oceans. The GTMBA data involve the Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) in the Pacific, the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), and the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean. We examine the IMERG error characterization and bias distribution across the daily, monthly, and seasonal scales over the tropical oceans. Subsequently, we investigate the IMERG performance for light and extreme precipitation, both in terms of intensity and frequency. The evaluation of the IMERG data with OceanRain and buoys constitute both point-area and grid-grid based approaches. The categorical indices, which used to evaluate the detection capability of IMERG include the Probability of Detection (POD), the False Alarm Ratio (FAS) and the Critical Success Index (CSI). This study will bring out important information for the user community, the GPM ground validation group, and algorithm developers regarding the IMERG performances and thus its applicability over an ‘untraditional’ region such as oceans.
Key words: GPM, IMERG, Precipitation, OceanRain, Buoys, Remote sensing
How to cite: Pradhan, R. K. and Markonis, Y.: Performance evaluation of GPM IMERG precipitation over the tropical oceans, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-463, https://doi.org/10.5194/egusphere-egu22-463, 2022.
Precipitation is an essential climatic variable for any hydrological study; however, obtaining continuous data of observed precipitation at a desirable resolution has been quite challenging. In this regard, satellite-based precipitation estimates play an important role in enhancing the present hydrologic prediction capability as they are mostly available at a high spatiotemporal resolution with global coverage. Without referring to ground measurements, satellite-based estimates can be biased and, although some gauge-adjusted satellite-precipitation products have already been developed, those need to be evaluated before any hydrological applications. In the present study, SM2RAIN-GPM rainfall product is evaluated for its performance with respect to the gauge-based India Meteorological Department (IMD) gridded dataset over the entire Indian region. The SM2RAIN-GPM dataset is the integration of SM2RAIN (bottom-up approach) based rainfall estimates derived from satellite soil moisture and Global Precipitation Measurement (GPM) based Integrated Multi-satellitE Retrievals for GPM (IMERG) early run product. The evaluation of the satellite-based daily rainfall estimates is carried out for 12 years (2007-2018) on the basis of qualitative and quantitative indicators. In general, the SM2RAIN-GPM rainfall product is excellent in detecting the daily rainfall events over India (mean and median probability of detection are 0.81 and 0.89, respectively), although some of the events are falsely detected (mean and median false alarm ratio are 0.47 and 0.46, respectively). Overall, a good agreement has been observed between satellite rainfall against IMD rainfall product with the mean and median Agreement Index as 0.7 and 0.74, respectively, whereas the median Kling-Gupta efficiency (KGE) is found to be 0.46. The mean absolute error in satellite rainfall is found to be in the range of 0.44 to 16.98 mm/day with a mean of 2.78 mm/day and a median of 2.43 mm/day. Further, the error has been decomposed into random and systematic components and it is found that the systematic error component is more dominant. Moreover, the percent bias (PBIAS) in satellite rainfall was found to be in the range of –97.25 to 201.91, while the RMSE to standard deviation ratio (RSR) ranged from 0.53 to 1.6. The mean and median values of PBIAS (RSR) are found to be 4.51 (0.79) and 7.91 (0.77), respectively. The precipitation product has higher under-hit and false biases than over-hit and miss biases. The performance of the product over the Himalayan region, North-eastern India, and the Western Ghats is relatively poor compared to other regions. The present study indicates that the SM2RAIN-GPM rainfall product is useful in hydrological studies of ungauged regions of India.
How to cite: Dayal, D., Pandey, A., and Gupta, P.: A Comprehensive Evaluation of SM2RAIN-GPM Precipitation Product over India , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-518, https://doi.org/10.5194/egusphere-egu22-518, 2022.
The Global Precipitation Climatology Project (GPCP) product is a popular combined satellite-gauge precipitation data set in which the long-term standards of consistency and homogeneity are underlined. Here we discuss various high latitude analyses considered in the recently released GPCP V3.2 monthly and daily products. Satellite data are used over land and ocean and obtained from the Special Sensor Microwave Imager (SSMI), Special Sensor Microwave Imager/Sounder (SSMIS), geostationary imagers and polar-orbiting infrared sounders. GPCP uses the Global Precipitation Climatology Centre (GPCC) over land, as its in situ component, but prior to combination with satellite data GPCC estimates are adjusted for gauge undercatch. Advanced sensors aboard the Tropical Rainfall Measuring Mission (TRMM), CloudSat, and Global Precipitation Measurement (GPM) mission have enabled more accurate estimation of rain and snowfall rates in recent years. Starting with GPCP V3.1 these observations are integrated into GPCP through the development of the Tropical Combined Climatology (TCC) used at lower latitudes and the Merged CloudSat, TRMM, and GPM (MCTG) climatology used over the extratropics and higher latitudes. Improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation are used outside 60ºN-S, where inside this zone the Goddard Profiling (GPROF) algorithm retrievals from SSMI/SSMIS is used to calibrate geostationary IR based precipitation estimate at monthly scale. The Gravity Recovery and Climate Experiment (GRACE) mass change observations are used to determine snowfall accumulations over frozen land and arctic basins and to assess gauge undercatch corrections. Observations of snow on sea ice from NASA’s Operation IceBridge (OIB) flights are utilized as an additional tool for snowfall assessment over sea ice. GPCP V3.2 has a higher spatial resolution (0.5ox0.5o) than earlier versions (e.g., 2.5ox2.5o in V2.3) over both land and ocean, going back to 1983. Version 3 Daily product uses the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates as well as rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.2 Monthly estimate. GPCP V3.2 shows about 5.5% increase in global oceanic precipitation and about 4 % increase over global land and ocean compared to the previous version (V2.3), some major changes occur over the ocean and around 40oS and 60 oS. We will discuss other important changes of GPCP V3.2, compared to the earlier versions, and our future plans. This includes a discussion of some challenges that the team had to deal with, such as consistencies in inter-annual variations of satellite precipitation products and modification of gauge undercatch correction methods.
How to cite: Behrangi, A., Huffman, G. J., Adler, R. F., Ehsani, M. R., Bolvin, D. T., Nelkin, E. L., and Gu, G.: The Latest GPCP products (V3.2) and high latitudes analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1220, https://doi.org/10.5194/egusphere-egu22-1220, 2022.
Along with Arctic warming, climate models project a strong increase in Arctic precipitation in the 21st century as well as an increase in the ratio of liquid to total precipitation. Studying past precipitation changes in relation to changes in the formation, extent and melt of seasonal snow can increase our understanding of the snow climatological impacts of the projected future precipitation changes. In this contribution, the link between past precipitation changes and snow conditions on Ammassalik Island, Southeast Greenland will be assessed with a combination of in-situ observations, results from a regional climate model and an integrated snow model. The performance of the snow model will be evaluated with newly established in situ snow height and snow water equivalent data. In the same way, output from the regional climate model is evaluated with automatically monitored precipitation and climate data from weather stations. Thereafter results from model runs of the two aforementioned models will be assessed together to explore the link between precipitation changes and changing snow conditions. A particular interest lies in understanding the shift in the rainfall-snowfall elevation boundary and related snowmelt, as our hypothesis is that more liquid precipitation on higher elevations will lead to increased snowmelt in this mountainous area.
How to cite: van der Schot, J., Schöner, W., Abermann, J., and Ferreira Da Silva, T. M.: Linking past precipitation changes with changing snow conditions on Ammassalik Island, Southeast Greenland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1726, https://doi.org/10.5194/egusphere-egu22-1726, 2022.
Accurate precipitation estimation with high spatial and temporal resolution is key to many applications including weather forecast, flood monitoring and the prediction of natural hazards such as the recent extreme weather events around the world. While weather radars are able to monitor the spatio-temporal dynamics of precipitation, they are expensive and sparsely deployed around the world.
Alternatively, existing ground terminals used in satellite communication services (e.g. broadband internet) have shown the potential to function as accurate rain sensors. By analyzing the carrier-to-noise (C/N) data between the satellite and ground terminal, the rain-induced signal attenuation is estimated. The relationship between the attenuation and rain rate at millimeter-wave then allows computation of the latter. To tackle the difficulty of detecting rainy events and rain-induced attenuation, machine learning approaches are often used to learn from measurements of co-located rain gauges. These methods utilize dense or long short-term memory networks taking a temporal sequence of C/N values from one terminal as input to obtain the local rain rate. So far, most approaches have investigated each ground terminal as an independent sensor, fusing them only after rain rate estimation in order to create two-dimensional (2-D) rain maps. Since neighboring terminals are not considered, the rain estimates suffer from local inconsistencies and malfunctioning terminals are harder to detect which further impacts the accuracy.
In this work, to achieve spatio-temporal consistency, we propose to estimate rainfall from a dense grid of ground terminals using graph-neural networks (GNN). By including neighboring terminal information directly in the estimation, rain rates are more consistent and malfunctions are easier to detect. We model local terminal neighborhoods in a GNN combined with one-dimensional convolutional neural networks taking the temporal sequence of C/N values of each terminal as input. The neural networks directly map C/N values to rain rates that are supervised during training using external rain gauge and weather radar data. After estimating rain rates for all terminals, 2-D rain maps are created by using ordinary kriging interpolation.
Initial results for January 12, 2021 storm event across the entire French metropolitan regions using 8000 active ground terminals indicate an improved average rain rate accuracy in comparison to weather radars. Furthermore, the resulting rain maps are significantly more spatio-temporally consistent compared to independent terminal approaches. These promising results allow rainfall estimation from satellite communication data to strongly complement the weather radar data or become a viable alternative in areas not covered with radars.
How to cite: Krebs, J., Mishra, K. V., Gharanjik, A., and Shankar, M. R. B.: Spatio-Temporal Rainfall Estimation from Communication Satellite Data using Graph Neural Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1803, https://doi.org/10.5194/egusphere-egu22-1803, 2022.
Weather forecast and climate models require good knowledge of the microphysical properties of atmospheric snow particles. For example, particle cross-sectional area and shape are especially important parameters that strongly affect the scattering properties of ice particles and consequently their response to remote sensing techniques. The fall speed and mass of ice particles are other important parameters. The fall speed affects the rate of removal of ice from numerical models. The particle mass is a key quantity that connects the cloud microphysical properties to radiative properties.
Measurements of snow particles using the ground-based in-situ instrument Dual Ice Crystal Imager (D-ICI) have been carried out in Kiruna, Sweden, during several winter seasons. The D-ICI takes high-resolution side- and top-view images of falling hydrometeors, from which maximum dimension (describing particle size), cross-sectional area, and fall speed of individual particles are determined. Images from 2014 to 2018 form the dataset that is analysed for relationships between the different microphysical properties. The analysis is performed as a function of snow particle shape after sorting particles into 15 different shape groups.
While particle mass can be easily estimated geometrically from the image data for the simpler shapes such as columns and plates, mass for particles with more complex shapes cannot. Thus, particle mass of all snow particles in our dataset is derived from the direct measurements of particle size, cross-sectional area, and fall speed. For this we use an approach that connects mass to fall speed using an empirical relationship between the dimensionless Reynolds and Best numbers. Consequently, the relation between mass and the other microphysical properties can be studied as a function of shape. In addition, by evaluating these relationships and comparison to relationships from literature, we can study the usability of this Reynolds-to-Best-number-approach for the different shapes.
In general, our results show, depending on shape, varying but moderately to strongly correlated relationships among particle size, cross-sectional area, and fall speed that also compare favourably with many previous studies. There are a few discrepancies that can be linked to certain shapes, in particular column- and needle-like shapes, which show poor correlations between fall speed and particle size. We speculate that maximum dimension is not suitable to represent particle size for these shapes. Inconsistencies between the different relationships found for the same shapes corroborate our hypothesis as they indicate that maximum dimension is not suitable to determine Reynolds number. Thus, the Reynolds-to-Best-number-approach works poorly for these shapes and mass cannot be determined accurately. However, column width, where available, is a better representative particle size. Using a selection of columns, for which the simple geometry allows the verification of the empirical Best number vs. Reynolds number relationship, we show that Reynolds number and fall speed are more closely related to the diameter of the basal facet (i.e. the column width) than the maximum dimension. The agreement with the empirical relationship is further improved using a modified Best number, a function of an area ratio based on the falling particle seen in the vertical direction.
How to cite: Kuhn, T., Vázquez-Martín, S., and Eliasson, S.: Mass of individual snow particles retrieved from measured fall speed for various shapes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1918, https://doi.org/10.5194/egusphere-egu22-1918, 2022.
In recent years, there has been growing interest in characterizing atmospheric conditions prior to rain events using integrated water vapor (IWV) derived from ground-based microwave radiometers (MWR). However, the occurrence of rainfall depends on a myriad of atmospheric parameters. This paper uses a composite analysis method to analyze various atmospheric parameters that affect rainfall over the Swiss Plateau during the period 2011-2020. 1199 rainfall events generated from the TROpospheric WAter RAdiometer (TROWARA) with a 7 s temporal resolution are combined with fields from weather station records. Different weather time evolution characteristics such as IWV, integrated liquid water (ILW), cloud-bottom infrared temperature (IRT) along with meteorological parameters, temperature, pressure, relative humidity, wind speed, and air density are identified before, during, and after rainfall. Regardless of seasonality or rainfall duration, a sharp increase in the IWV, ILW, and IRT before rain, and all the meteorological parameters reach the extreme 0.5 to 1 hour before rain starts. IWV at the end of the rain is lower than at the beginning, and it filtered by the 10-min band pass filter fluctuates significantly before rain. Air density drops 2 to 6 hours before rain starts. The true detection rate for rainfall prediction from air density alone as one of the precursors reaches 60%. Applying all these parameters to jointly predict rainfall is possible to obtain higher prediction accuracy.
How to cite: Wang, W. and Hocke, K.: Atmospheric effects and precursors of rainfall over the Swiss Plateau, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1950, https://doi.org/10.5194/egusphere-egu22-1950, 2022.
Quantitative Precipitation Estimates (QPE) from the Integrated Multisatellite Retrievals for GPM (IMERG) provide crucial information about the spatio-temporal distribution of precipitation in areas with complex orography such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia (XEMA) is used to assess the performance of three IMERG products (Early, Late and Final). The analysis at different time scales, considered three terrain features (valley, flat and ridgetop) and five different categories related to rainfall intensity (light, moderate, intense, very intense, and torrential). During the period 2015-2020, IMERG derived-estimates reproduce well the spatial variability of the precipitation field in the region, although it shows some discrepancies, which become more evident with the reduction of the time scale. Except at sub-daily scales, all three products tend to overestimate by more than 20% records of rain-gauges located in flat areas. The correlation coefficient (r) reflects the improvement of IMERG with increasing time scale with values above 0.7 at annual scale and values just above 0.35 at sub-daily scale. On this scale, rainfall classified as very heavy and torrential showed the poorest results, with significant underestimates higher than 80 %. This weakness of IMERG products is most evident in the IMERG Final, which although providing a reliable reproduction within the interquartile range of the distribution, is not able to detect extremes at different scales. This is related to the inadequate number of Global Precipitation Climatology Centre (GPCC) stations used for calibration. Despite the shortcomings, it can be concluded that IMERG is a valuable tool for the analysis of hydrometeorological processes and useful to complement research in the branches of weather and climate in Catalonia. This research was partly funded by the project “Analysis of Precipitation Processes in the Eastern Ebro Subbasin” (WISE-PreP, RTI2018-098693-B-C32, MINECO/FEDER) and the Water Research Institute (IdRA) of the University of Barcelona.
How to cite: Peinó, E., Bech, J., and Udina, M.: Assessment of GPM- IMERG precipitation products over Catalonia at different time resolutions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2933, https://doi.org/10.5194/egusphere-egu22-2933, 2022.
The Global Precipitation Measurement (GPM) Mission Validation Network (VN) is a NASA software system run at Marshall Space Flight Center which geometrically matches three-dimensional precipitation retrievals from the GPM Core Observatory (CO) sensors to 118 international ground-based radars. To advance the capabilities for validation of the multi-satellite IMERG product, the GPM VN is being updated to integrate this Level-3 (gridded) product alongside GPM’s Level-2 (footprint) products (DPR, CORRA, GPROF). The updated GPM VN will enable the potential for tracing the origins of systematic and random errors back through IMERG into the source GPROF product at instances of GPM-CO overpasses. Furthermore, the GPM VN can support validation efforts to trace the origins of IMERG inaccuracies under a consistent framework across locations including North America (Eastern CONUS, Alaska, Hawaii), Brazil, and Pacific islands (e.g. Kwajalein). This first study with the updated GPM VN will assess the oceanic performance of IMERG V06B across different island sites, as well as stratify errors using the vertical profile of reflectivity and hydrometeor classification corresponding to the IMERG grid pixel. These results will help to inform improvements for future IMERG versions, as well as to aid the community in understanding the conditions under which IMERG can be aptly applied for research and societal applications.
How to cite: Watters, D., Gatlin, P., Kirstetter, P., Huffman, G., Tan, J., Nelkin, E., Bolvin, D., Wolff, D., and Wang, J.: IMERG Validation with the GPM Validation Network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3144, https://doi.org/10.5194/egusphere-egu22-3144, 2022.
Recent studies indicate that precipitation systems causing heavy rainfall affecting a wide area in Japan show similar characteristics as organized precipitation systems with deep inflow layers (Hamada and Takayabu 2018; Tsuji et al. 2021). Free-tropospheric moisture is a key factor for organizing such precipitation systems (e.g., Holloway and Neelin 2009). However, less attention has been paid to the roles of free-tropospheric moisture on precipitation systems causing heavy rainfall events around Japan.
In this study, contributions of each term in the water vapor budget equation are investigated for an extreme rainfall event that occurred in July 2020 over Kyushu, Japan. To focus on the roles of free-tropospheric moisture, the vertically integrated water vapor flux convergence (IVFC) term is divided into the boundary-layer and free-troposphere by 900 hPa.
The free-tropospheric IVFC starts to increase over one day before the rainfall peak time. Change in the precipitable water tendency follows the increase of the free-tropospheric IVFC. Further analyses with dividing the IVFC into an advection term (V∇q) and a divergence term (q∇V) clarify that the change in the advection term corresponds to that in the precipitable water tendency. A synoptic disturbance is developed over China and propagated eastward when the precipitable water tendency increases. This synoptic disturbance enhances the moisture advection, moistening the atmosphere over Kyushu before the rainfall event. Under the moistened environment, a mesoscale convective system (MCS) starts to develop nine hours before the rainfall peak time. The MCS covers Kyushu Island at the rainfall peak time, and intense precipitation areas appear to the southern edge of the MCS, causing disastrous rainfall. Vertical cross-sections of the MCS show a slantly ascending deep inflow layer with moist absolutely unstable layer (MAUL), consistent with organized precipitation systems shown in previous studies (Bryan and Fritsch 2000).
These results indicate that the free-tropospheric IVFC contributes to the heavy rainfall event by providing environments favorable for producing and maintaining deep inflow structure and MAUL, which characterize organized precipitation systems.
Acknowledgments
This research is supported by Japan Aerospace Exploration Agency (JAXA) Precipitation Measuring Mission science, the University of Tokyo through a project “Research hub for the big data analysis of global water cycle and precipitation in changing climate”, and the Environment Research and Technology Development Fund (JPMEERF20192004) of the Environmental Restoration and Conservation Agency of Japan.
References
Bryan, G. H., and J. M. Fritsch, 2000: Moist absolute instability: The sixth static stability state, Bull. Amer. Meteor. Soc., 81(6), 1207-1230, doi:10.1175/1520-0477(2000)081<1287:MAITSS>2.3.CO;2
Hamada, A., and Y. N. Takayabu, 2018: Large-scale environmental conditions related to midsummer extreme rainfall events around Japan in the TRMM region. J. Climate, 31(17), 6933–6945. doi:10.1175/JCLI-D-17-0632.1
Holloway, C. E., and J. D. Neelin, 2009: Moisture vertical structure, column water vapor, and tropical deep convection. J. Atmos. Sci., 66(6), 1665–1683. doi:10.1175/2008JAS2806.1
Tsuji, H., Y. N. Takayabu, R. Shibuya, H. Kamahori, and C. Yokoyama, 2021: The role of free-tropospheric moisture convergence for summertime heavy rainfall in western Japan. Geophys. Res. Lett., 48, e2021GL095030. doi:10.1029/2021GL095030
How to cite: Tsuji, H. and Takayabu, Y. N.: A case study of heavy rainfall event in July 2020 over western Japan focusing on free-tropospheric moisture, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3294, https://doi.org/10.5194/egusphere-egu22-3294, 2022.
The Global Satellite Mapping for Precipitation (GSMaP) produces high-resolution and high-frequent global rainfall map based on multi-satellite passive microwave radiometer observations with information from the Geostationary InfraRed (IR) instruments (Kubota et al. 2020). Output product of GSMaP algorithm is 0.1-degree grid for horizontal resolution and 1-hour for temporal resolution. The GSMaP near-real-time version product (GSMaP_NRT) has been in operation at JAXA since November 2007 in near-real-time basis, and browse images and binary data available at JAXA GSMaP web site (http://sharaku.eorc.jaxa.jp/GSMaP/).
A new version of the GSMaP product was released in December 2021. We plan the reprocessing of the GSMaP standard version in a period during the past 24 years since Jan. 1998. The GSMaP algorithms consist of passive microwave (PMW) algorithms, a normalization module for PMW retrievals, a PMW-IR Combined algorithm, and a Gauge-adjustment algorithm. Features in the new version are summarized as follows. In the PMW algorithm, retrievals extended to the pole-to-pole. Databases used in the algorithm were updated. A method using frozen precipitation depths was newly installed (Aonashi et al. 2021). Heavy orographic rainfall retrievals were improved upon a basic idea of Shige and Kummerow (2016). The normalization module for PMW retrievals (Yamamoto and Kubota 2020) were newly implemented to make more homogeneous PMW retrievals, in particular, for microwave sounders. A basic idea of the PMW-IR combined algorithm is using morphing and Kalman filter (Ushio et al. 2009). In addition, a histogram matching method by Hirose et al. (2022) was implemented in the new version to reduce the IR retrievals with reference to the PMW retrievals. In the gauge-adjustment algorithm, a precipitation estimate is adjusted using the NOAA CPC Global Unified Gauge-Based Analysis of Daily Precipitation (Mega et al. 2019). Artificial patterns appeared in past versions were mitigated in the new version. Preliminary validation results using the gauge-adjustment ground radar data over the Japan land areas confirmed better results in the new version of the satellite only products.
Furthermore, the GSMaP real-time version (GSMaP_NOW) with the new algorithm was also released in December 2021.The GSMaP algorithm for the new version was also applied to the GSMaP_NOW system after 6th December 2021. Accuracy improvements were confirmed also in the GSMaP_NOW products by validations with the gauge-adjustment ground radar data over Japan.
How to cite: Kubota, T., Aonashi, K., Ushio, T., Shige, S., Yamaji, M., Yamamoto, M., Hirose, H., and Takayabu, Y.: A new version of Global Satellite Mapping of Precipitation (GSMaP) product released in December 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3328, https://doi.org/10.5194/egusphere-egu22-3328, 2022.
Better understanding and modeling snowfall microphysical properties and processes is a key challenge in atmospheric science, crucial for snowfall quantification, remote sensing, and weather prediction in general.
The use of meteorological radars for this intent has become quite popular, in particular through two techniques: the use of multi-frequency radar variables on the one hand, and of radar Doppler spectra on the other. Combining both techniques is however a challenging task, complicated by the variability of ice crystals properties and atmospheric conditions, in addition to measurement errors and artifacts such as radars' imperfect calibration and beam matching. We propose a novel approach to retrieve snowfall microphysical properties, by making the most of dual-frequency Doppler spectrograms while relaxing some assumptions on beam-matching and non-turbulent atmosphere.
The technique is based on a two-step deep-learning framework inspired from auto-encoder models, which are generally used for dimension reduction purposes: an encoder maps high-dimensional data to a lower-dimensional “latent” space, while the decoder tries to recover the original signal from this latent space. In the proposed framework, dual-frequency Doppler spectrograms constitute the high-dimensional input, while the dimensions of the latent space are constrained to represent the snowfall properties which we seek to retrieve.
As a first step, a decoder neural network is trained to generate Doppler spectra from a given set of microphysical variables, using simulations from the radiative transfer model PAMTRA as training data. In a second step, the encoder network learns the inverse mapping, from dual-frequency spectrograms to the microphysical latent space. It is trained on real data, and outputs values in the latent space which, when passed as input to the decoder – whose parameters are now frozen – yield reconstructed spectrograms that should match the original data.
In comparison with classical methods, which provide a direct gate-to-gate inversion of the problem, the proposed framework allows to take into account the spatial continuity of the microphysical variables by using convolutions in the architecture of the models, thereby reducing the ill-posedness of the problem.
The method was implemented on X- and W-band data from the ICE GENESIS campaign that took place in January 2021 in the Swiss Jura, and showed promising results. Comparisons with in-situ airborne data also collected during the campaign allow for in-depth assessments of the performance of the algorithm.
How to cite: Billault–Roux, A.-C., Ghiggi, G., and Berne, A.: Dual-frequency spectral radar retrieval of snowfall microphysics: a deep-learning based approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4296, https://doi.org/10.5194/egusphere-egu22-4296, 2022.
Gridded observational datasets are often used for the evaluation of climate simulations. However, uncertainty originating from the selection of observations is as important as the uncertainty of regional climate models. In this study, we introduce a novel evaluation method assessing the uncertainty of observational datasets. For this, various metrics i.e. relative difference and root-mean-square error are also used, and statistical techniques i.e. correlation analysis and permutation test were carried out. We focused on the Carpathian region, which is located in eastern-central Europe. The method is applied to the observational datasets CarpatClim and E-OBS for 2010 – the wettest year in the region since the beginning of the regular measurements. For the comprehensive analysis, not only precipitation and temperature were used, but also geographic variables (elevation, the variability of elevation, and the effect of station density). The evaluation method can be applied to other datasets, different time periods and geographical areas, moreover, it is also appropriate to find errors and shortcomings in the datasets. Based on our findings, CarpatClim is wetter over the whole region (mostly over mountains) than E-OBS. The temperature fields are similar in the two datasets, however, E-OBS is a little warmer than CarpatClim over the mountainous areas. The results show that precipitation depends on station density, while the most important variable for temperature is elevation. The study points out that the choice of reference could have an important effect on the validation of climate simulations and therefore it is essential to take observational uncertainty into account.
How to cite: Kalmár, T., Kristóf, E., Hollós, R., Pieczka, I., and Pongrácz, R.: Quantifying uncertainties in observational datasets over the Carpathian region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4421, https://doi.org/10.5194/egusphere-egu22-4421, 2022.
This aim of this study is to examine whether different climate scenarios, as they are adopted in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), lead to different modes of the energetics components of the Lorenz’s energy cycle which would therefore have an impact on the “rate of working” of the climate system. In particular, the study focuses on four energy forms, namely the Zonal and Eddy components of both the Available Potential and Kinetic Energies, the permissible conversions between these forms of energy, the diabatic generation of Available Potential Energy as well as the dissipation of the Kinetic Energy.
The CMIP6 climate projections in the 85-year period from 2015 to 2100 produced by the HadGEM3-GC3.1 model have been used. These projections are driven by a set of Shared Socioeconomic Pathways (SSP’s) based on new future pathways of societal development but also incorporating the previously used Representative Concentration Pathways (RCPs). In this respect, the following three concentration-driven scenarios under Tier 1 of ScenarioMIP are used:
ssp126: Α scenario with low radiative forcing by the end of the century, following the RCP2.6 global forcing with SSP1 socioeconomic conditions; radiative forcing reaches a level of 2.6 W/m2 in 2100;
ssp245: Α scenario with medium radiative forcing by the end of the century, following the RCP4.5 global forcing with SSP2 socioeconomic conditions; radiative forcing reaches a level of 4.5 W/m2 in 2100;
ssp585: Α scenario with high radiative forcing by the end of century, following the RCP8.5 global forcing with SSP5 socioeconomic conditions.
For comparative purposes, the corresponding historical 85-year dataset, preceding the time period covered by the climatic projections has been used. In this respect, data from the same cohort in the period 2029 to 2014 form also part of this study.
The energy balance and time series of the energetics components under different SSP-based scenarios show that different scenarios yield diverse energetics regimes, consequently impacting the Lorenz’s energy cycle and its underlying physical processes.
How to cite: Michaelides, S.: Modes of Lorenz atmospheric energetics under different CMIP6 climate scenarios, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4913, https://doi.org/10.5194/egusphere-egu22-4913, 2022.
The estimation of precipitation from satellite sensors is crucial for measuring precipitation at the global scale. Due to the variability of precipitation, both temporally and spatially, it is necessary to exploit observations from both passive microwave imaging and sounding instruments, as well as visible/infrared observations. While visible/infrared techniques provide frequent sampling with reasonable resolution, the relationship between the cloud top properties and surface precipitation are often poor. Passive microwave observations are sensitive to the presence of the precipitation particles themselves and therefore the observations are more directly related to the precipitation at the surface. Exploiting both passive microwave imagers and sounders is necessary to ensure reasonable temporal sampling. The compact nature of passive microwave sounders has allowed these sensors to be developed for cubesats, such as the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission. This mission comprises of a total of 7 cubesats, an initial pathfinder launched in June 2021, followed by 6 more to be launched mid-2022. The pathfinder is currently in a polar orbit while the following 6 satellites will be in a low-inclination orbit, providing frequent observations across the Tropics. Each cubesat carries a passive microwave sounder gathering observations from 91.665 GHz to 204.8 GHz in a cross-track scanning mode with spatial resolutions similar to the current Microwave Humidity Sounder sensors. The Precipitation Retrieval and Profiling Scheme (PRPS), initially developed for the larger sounding instruments, has been adapted for use with the TROPICS observations. The PRPS uses an a priori database against which observed radiances are compared and the associated precipitation intensities retrieved. Initial results from the pathfinder will be presented, together with validation against surface reference data sets. These results are promising and show that the retrievals are comparable with other passive microwave sounding instruments.
How to cite: Kidd, C. and Matsui, T.: Precipitation retrievals from cross-track sensors: initial results and validation of the TROPICS precipitation product., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6660, https://doi.org/10.5194/egusphere-egu22-6660, 2022.
Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D) is a nearly 3-year NASA mission to demonstrate global observations from a multi-frequency microwave sensor deployed on a 6U CubeSat platform. TEMPEST was proposed to Earth Venture Instrument-2 in 2013 to perform high temporal resolution observations of rapidly evolving storms using a constellation of five 6U CubeSats with identical microwave sensors in a single orbital plane, providing 7-minute temporal sampling of rapidly-developing convective activity over 30 minutes. To demonstrate necessary capability for TEMPEST constellation operation, NASA’s Earth Venture Technology program funded the TEMPEST-D mission, a multi-frequency microwave radiometer on a single 6U CubeSat, successfully delivered for launch less than 2 years after PDR.
TEMPEST-D was deployed from the ISS into low Earth orbit on July 13, 2018, and observed the Earth’s atmosphere nearly continuously until it re-entered on June 21, 2021. TEMPEST-D performed the first global Earth observations from a multi-frequency microwave radiometer on a CubeSat. The TEMPEST-D mission substantially exceeded expectations of data quality, stability, consistency and mission duration. TEMPEST-D data were validated using the double-difference technique for cross-calibration with scientific and operational microwave sensors observing at similar frequencies, including 4 MHS sensors on NOAA-19, MetOp-A, -B and -C, as well as GPM/GMI. These validation results showed that TEMPEST had comparable or better performance to much larger operational sensors in terms of calibration accuracy, precision and stability throughout the nearly 3-year mission.
TEMPEST-D performed detailed observations of the microphysics of hurricanes, typhoons and tropical cyclones during three consecutive hurricane seasons. Simultaneous observations by TEMPEST-D and JPL’s RainCube weather radar demonstrated physical consistency and well-correlated passive and active microwave measurements of severe weather from the two CubeSats. Quantitative precipitation estimates retrieved from TEMPEST-D data are highly correlated with standard ground radar precipitation products, such as NOAA/NWS MRMS. TEMPEST-D also periodically performed along-track scanning measurements to provide the first space-borne demonstration of “hyperspectral” microwave sounding observations to retrieve the height of the planetary boundary layer.
The stability, accuracy and reliability of TEMPEST-D on a 6U CubeSat open a breadth of possibilities for future Earth observation and science missions on small satellites to enable rapid temporal observations of cloud and precipitation processes. Early in the development of the TEMPEST-D mission, a nearly identical microwave sensor, TEMPEST-D2, was produced alongside the original to reduce risk from the original manifest for launch. TEMPEST-D2 was delivered to the U.S. Space Force in 2021 for integration with the Compact Ocean Wind Vector Radiometer (COWVR), previously developed by NASA/Caltech JPL. On December 21, 2021, COWVR and TEMPEST-D2 were launched from KSC as part of the Space Test Program (STP-H8) mission for at least 3 years of operations on the ISS. These two passive microwave sensors provide a unique, synergistic opportunity for coordinated global observations of the Earth’s oceans and atmosphere using complementary small satellite instruments. Finally, the demonstrated success of TEMPEST-D and RainCube was essential in NASA’s selection in November 2021 of the Investigation of Convective Updrafts (INCUS) mission as Earth Venture Mission-3, to be launched in 2027.
How to cite: Reising, S. C., Chandrasekar, C. V., Brown, S. T., Berg, W., Kummerow, C. D., Gaier, T. C., Padmanabhan, S., and Radhakrishnan, C.: Enabling Temporal Observations of Cloud and Precipitation Processes using Small Satellites: TEMPEST-D Demonstration on a CubeSat for 3 Years and Follow-on Missions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6689, https://doi.org/10.5194/egusphere-egu22-6689, 2022.
Severe thunderstorms develop over La Plata basin, in southeastern South America, more often during austral wintertime, between June and August. These systems have significant socioeconomic impacts over the region, and, therefore, a better understanding of how atmospheric drivers modulate their formation is important to improve the forecast of such phenomena. In this study, we selected a hailstorm event observed over southeastern La Plata basin during 14-15 July 2016, and simulated it using three Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) model configurations, each driven by a different global forcing: Global Forecast System (GFS), Climate Forecast System version 2 (CFSv2), and ECMWF Reanalysis v5 (ERA5). The ability of the BRAMS model in simulating cloud microphysical properties was evaluated by comparing the model output with satellite- and radar-based observations. Model results showed good skill in capturing the basic characteristics of the thunderstorm in terms of the spatial distribution of hydrometeors. The location of the maximum concentrations of hydrometeors was realistically represented by all simulations; however, slight to moderate differences in cloud properties between observations and model simulations were observed, with BRAMS/CFSv2 and BRAMS/ERA5 simulations performing best and worst, respectively, against measurements. In addition, these two simulations were able to reproduce ground-level hail concentrations over some of the reported hail fall areas. This study provides a first assessment of the BRAMS model to reproduce microphysical features of a severe thunderstorm captured by remote sensing observations over southeastern La Plata basin, one of the most hail-damage prone areas in the world.
How to cite: Vara-Vela, A., Crespo, N., Benavente, N., Bueno de Morais, M., Martins, J., Phillips, V., Gonçalves, F., and da Silva Dias, M.: Bulk cloud microphysical properties as seen from numerical simulation and remote sensing products: case study of a hailstorm event over La Plata basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6739, https://doi.org/10.5194/egusphere-egu22-6739, 2022.
Understanding precipitation variability is challenging, particularly in regions such as New Zealand, where the local topography strongly controls precipitation. This study provides a comprehensive evaluation of mean and extreme precipitation features over Aotearoa, New Zealand, using six merged satellite-gauge, five reanalysis, and three in-situ products, using station data as a reference. We find that all products show similar features in depicting the mean precipitation pattern with a clear maximum over the Alpine regions and a strong west-east gradient across the South Island. However, there are differences in the magnitude of mean precipitation estimates amongst different products at various regions on a seasonal timescale. Investigating the frequency of wet days shows that GPCP has the lowest count of all the satellite-based products, likely due to its coarse grid size, while MSWEP depicts the highest frequencies over the wettest region. Similarly, for reanalysis, MERRA-TP likely overestimates the frequency of occurrence west of the Alps compared to MERRA-PCORR, although the former showed better similarity with other datasets in terms of mean precipitation pattern. Moreover, statistical tests such as the Pearson correlation coefficient of the spatiotemporal pattern revealed that amongst the satellite-based products, MSWEP and GPM-IMERGE outperform other products with values of 0.9 and 0.66 with a mean wet-bias of 0.37 and 0.89 mm/day over the entire country. At the same time, ERA-5 and BARRA-R perform better in the suite of reanalysis products with a mean correlation coefficient of 0.87 and 0.74 with a mean wet-bias of 0.43 and 0.76 mm/day, respectively. This presentation also incorporates a set of precipitation indices approved by the ETCCDI committee to facilitate an intercomparison of different products in capturing the extreme tail of the distribution. Substantial differences especially over the West Coast in the South Island were observed in the interannual variability of the indices among different products. A closer examination of the percentile-based indices such as R95P and R99P revealed a contrasting pattern between different products in geographical regions. For instance, all the satellite-based products consistently showed wet bias as compared to the reanalysis products that depicted dry bias in all seasons. The MSWEP and BARRA-R datasets had the smallest relative percentage difference compared with the station data for most of the indices, suggesting their potential use for capturing both the mean and extremes characteristics of precipitation quite well in this region.
How to cite: Vishwanathan, G., McDonald, A., Stone, D., Rosier, S., Rana, S., and Noble, C.: Evaluation of satellite, gauge and reanalysis precipitation products over Aotearoa, New Zealand , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6842, https://doi.org/10.5194/egusphere-egu22-6842, 2022.
The theory of extreme precipitation has come to a more mature state over the last decade and highlights the balance between the change in extreme precipitation and in surface humidity with warming. The latter is further constrained by the changes in surface temperature. The analytically derived scaling coefficient based on the Clausius-Clapeyron derivative is ~6 %.K−1 under typical tropical surface conditions. While frequently confronted with observations over land, the theory has so far only been marginally evaluated against precipitation data over the ocean. Using an ensemble of satellite-based precipitation products and a suite of satellite-based sea-surface temperature (SST) analyses at 1°-1day resolution, extreme scaling is investigated for the tropical ocean (30°S – 30°N). The focus is on the robust features common to all precipitation and SST products. It is shown in this study that microwave constellation-based precipitation products are characterized by a very robust positive scaling over the 300 – 302.5K range of 2-days-lagged SST. This SST range corresponds to roughly 60 % of the amount of tropical precipitation. The ensemble mean scaling lies around the theoretically expected rate of 6 %.K−1 regardless of the extreme indices computed or the length of the period considered. The robustness of the results confirms the suitability of the current generation of constellation-based precipitation products for extreme precipitation analysis. Furthermore, the ability of the RCEMIP models to properly simulate the observed behavior with tropical SST is discussed.
How to cite: De Meyer, V., Roca, R., and Fiolleau, T.: Thermodynamic Scaling of Extreme Daily Precipitation over the Tropical Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7898, https://doi.org/10.5194/egusphere-egu22-7898, 2022.
In July 2021, western Europe has been subject to extreme rainfall that lead to severe flooding, incurring heavy property losses and claiming dozens of people’s lives in both Germany and Belgium. This unfortunate disaster is a reminder that carrying out studies about extreme event forecasting is a matter of prime importance in the field of meteorology. The extreme rainfall from July 2021 invites us to study the performance of our deep learning method for precipitation estimation in case of extreme events.
The main novelty of our method resides in its ability to merge different physical measurement modalities in order to improve precipitation estimation accuracy. In specific, the proposed method merges rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network design, composed of an encoder–decoder architecture, performs multiscale analysis of the three input modalities to simultaneously estimate the rainfall probability and the precipitation rate with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Once trained, we evaluate the performance of our model to estimate the extreme precipitation that happened in Belgium and Germany in July 2021 by comparing our results with the measurements from rain gauges and radar estimation.
How to cite: Moraux, A., Dewitte, S., and Munteanu, A.: A deep learning multimodal method for precipitation estimation: case study of the extreme rainfall from July 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8097, https://doi.org/10.5194/egusphere-egu22-8097, 2022.
Severe thunderstorms and associated weather phenomena like large hail, heavy precipitation and strong winds pose a substantial threat to public safety and infrastructure. Thunderstorms are commonly monitored using C- or S-band weather radars with update times between 2 to 5 minutes for volumetric scanning, and range resolutions in the order of 250 to 500m. Recent studies, however, suggest potential benefits of rapid-update dual-pol radar observations for operational nowcasting and the understanding of microphysical processes in thunderstorms.
Since early 2020, the University of Natural Resources and Life Sciences in Vienna operates a mobile, dual-pol X-band radar. While its range is limited to 50 km - significantly less than the maximum range of conventional C- or S-band radars - it provides a radial resolution of 50 m and update times of 1 min for volumetric scans with up to 8 elevation angles.
We present detailed observations of two severe thunderstorms passing over Vienna: A supercell in June 2020 producing hailstones of up to 4 cm diameter, and a squall line in July 2020 with wind gusts up to 40 knots. Both systems showed typical polarimetric signatures of heavy storms such as ZDR columns, albeit with large differences regarding their temporal evolution and their location within the storm. In addition, a dual-Doppler retrieval of the three-dimensional wind field using data of the C-band radar at Vienna airport was conducted to examine the storm dynamics.
How to cite: Klaus, V., Kaltenböck, R., and Rieder, H.: Rapid-update X-band radar observations of two severe storms in Vienna, Austria, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8248, https://doi.org/10.5194/egusphere-egu22-8248, 2022.
As part of the “extended operations” past the 3-year prime mission, the Global Precipitation Measurement (GPM) mission continues to develop improved products, currently rolling out the next Version 07 datasets. This is later than expected, due to unforeseen complications in upgrading algorithms. Example upgrades include: Complete data across the shift in scanning strategy by the Dual-frequency Precipitation Radar is now provided. The Goddard Profiling (GPROF) algorithm is improved in regions where orographic enhancement and suppression take place, or where the surface is snowy/icy. One key point is ensuring continuity across the boundary between the Tropical Rainfall Measuring Mission (TRMM) and of the GPM Core Observatory for each product. As well, analyses by users have directly affected algorithm development. Specifically, user research on precipitation features in the Integrated Multi-satellitE Retrievals for GPM (IMERG) led to findings on how the forward/backward morphing process and Kalman filter (KF) weighting distorts the Probability Density Function (PDF) of regional precipitation rates. This insight has led to the Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood (SHARPEN), a regional adjustment to the PDF of KF precipitation estimates. In another initiative, the IMERG team worked with a user to develop the Histogram Anomaly Time Series analysis, providing a simple summary of the time series of anomalies in the PDF of precipitation over a region, and revealing natural and input-based variations in precipitation.
We will report the status of GPM Version 07 processing as of the conference time, and provide some examples of the changes in algorithm performance between Versions 06 and 07.
How to cite: Huffman, G., Braun, S., Bolvin, D., Nelkin, E., and Tan, J.: Status and Plans for GPM and IMERG as They Enter Version 07, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8362, https://doi.org/10.5194/egusphere-egu22-8362, 2022.
Validation of Hail Identification Algorithm for GPM DPR (version 7)
- Chandrasekar 1, 2 and Minda Le 1
Colorado State University
Finnish Meteorological Institute
Extreme precipitation such as hail has raised interest due to its huge impact to human activities. In the new version of GPM DPR algorithm (version 7), a new Boolean hail product is developed to identify hail along a vertical profile. The main feature of this algorithm is for the first time, offers the potential of retrieving a uniform and homogeneous hail dataset on the global scale from radar sensors. The algorithm is built upon the precipitation type index (PTI). PTI is a value calculated for each dual-frequency profile with precipitation observed by GPM DPR. The dual-frequency ratio slope with respect to height, the maximum of reflectivity and storm top height are three key ingredients composing PTI value.
PTI has been shown to be effective in separating various precipitation types such as snow, graupel and hail profiles [1][2][3]. In this research, we focus on validation of hail identification algorithm by analyzing and cross-validating hail observations from various sources including individual hailstorm and on a global scale. Our algorithm will be validating with hailstorms observed by ground validation radar NEXRAD, GMI based hail identification and multiple scattering effect from Trigger module output of DPR level-2 algorithm. The global scale analysis is essential for satellite-based products. We validation this hail product with various global hail maps using radar, radiometer-based algorithms and reports.
[1] Le, M and V. Chandrasekar, Graupel and Hail Identification Algorithm for the Dual-frequency Precipitation Radar (DPR) on the GPM Core Satellite. J. Meteor. Soc. Japan, Vol. 99, 2021.
[2] Le, M. and V. Chandrasekar, Ground Validation of Surface Snowfall Algorithm in GPM Dual-Frequency Precipitation Radar. J. Atmos. Oceanic Technol., no 36, pp. 607–619, 2019.
[3] Le, M. and V. Chandrasekar, A New Hail Product for GPM DPR Algorithm. IGARSS’, 2021, Jul 12th ~ 16th, Brussels.
How to cite: Chandrasekar, C. V.: Validation of Hail Identification Algorithm for GPM DPR (version 7), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8996, https://doi.org/10.5194/egusphere-egu22-8996, 2022.
The Lower Mekong region is particularly prone to natural hazards caused by extreme rainfall. During monsoon seasons from June to October, heavy rainfall triggers severe flash floods and landslides, posing a threat to human lives and livelihoods. Global forecast products struggle to provide reliable estimates of extreme rainfall at regional scale, which is a big challenge in their integration in early warning systems. A newly released version of Climate Hazards Center InfraRed Precipitation with Stations (CHIRPS) Global Ensemble Forecast System (GEFS) dataset is a bias-corrected and downscaled product derived from National Centers for Environmental Prediction (NCEP)-GEFS. CHIRPS-GEFS product provides up to 16 days of rainfall forecasts at 5km/daily spatio/temporal resolution. This study evaluates the spatial and temporal performance of the CHIRPS-GEFS for extreme precipitation in the Lower Mekong region during monsoon seasons from 2014 to 2019. Rainfall forecasts from 1 to 5-days lead-time are analyzed against the bias-corrected Integrated Multi-satellitE Retrievals for GPM (IMERG) over the lower Mekong region. The performance is assessed using both categorical and continuous statistics such as the probability of detection, false alarm ratio, critical success index, correlation coefficient, and root mean squared error. Results describe the spatial and temporal strengths and limitations of the CHIRPS-GEFS and the influence of geomorphological conditions on its performance. This analysis provides valuable information on CHIRPS-GEFS possible integration in the lower Mekong landslide forecasting model for region-based landslide hazard assessment and situational awareness (LHASA) along the lines of the global LHASA framework.
How to cite: Laverde-Barajas, M., khan, S., Kirschbaum, D., Stanley, T., Meechaiya, C., Jayasinghe, S., and Towashiraporn, P.: Assessing the viability of using CHIRPS-GEFS for landslide forecasting in the Lower Mekong Region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9075, https://doi.org/10.5194/egusphere-egu22-9075, 2022.
Accurate prediction and modeling of heavy precipitation events remains an issue due to gaps in our understanding of the physical processes underlying them. Such gaps arise from the limited number of good quality observations that constrain the thermodynamic parameters (e.g. temperature, moisture, etc.) within heavy precipitation, since the observations of some space-based sensors are degraded in the presence of thick clouds.
The Global Navigation Satellite System (GNSS) Polarimetric Radio Occultation (PRO) technique was recently created to overcome some of these limitations, by providing vertical profiles of temperature, pressure, and water vapor, along with vertical information about hydrometeors (i.e. raindrops, snow, ice crystals, etc), simultaneously. It represents an enhancement of the standard radio occultation technique, that consists on tracking the signals emitted by GPS satellites from a low Earth orbit satellite occulting behind the Earth’s horizon. These signals cross lower and denser layers of the atmosphere as the occultation advances. The augmentation that the polarimetry provides consists on collecting these signals using two linearly and orthogonal polarized antennas (H and V), instead of the circularly polarized one used in the standard technique. Comparing the phase of the signals received at the two antenna ports, we can infer the presence of hydrometeors along the ray paths. Polarimetric Radio Occulation technique is being proved aboard the PAZ satellite, in an experiment led by the Institut de Ciències de l’Espai (ICE-CSIC,IEEC), in collaboration with NOAA, UCAR and NASA/Jet Propulsion Laboratory, operating since 2018. These profiles are obtained globally, through all kinds of clouds and over all kinds of surfaces. Such characteristics are rather unique in the current observing system.
For this study, mesoscale convective systems (MCS) are particularly of interest. Given the characteristics of the observational technique and the targeted systems, it is relatively easy to find collocated measurements. Therefore, we can study the nature of the vertical structure of the hydrometeors within the cloud structure of MCS, depending on their life stage (e.g. initiation, maturity, decay) and relative position (e.g. leading vs trailing), with the help of geostationary infrared imagery. Unique insights obtained with the new PRO technique will be presented.
How to cite: Padullés, R., Paz, A., Cardellach, E., Turk, F. J., Ao, C. O., de la Torre, M., Wang, K.-N., Oyola, M., and Schiro, K.: On the relationship between Polarimetric Radio Occultation observables and water content for convective systems at different life stages, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9410, https://doi.org/10.5194/egusphere-egu22-9410, 2022.
To improve rainfall monitoring from Commercial Microwave Links (CMLs) in a (sub)tropical climate, we adjust several parameters in the open-source R package RAINLINK that is used to retrieve rainfall rates from signal attenuation in cellular telecommunication networks.
These parameters related to local CML network characteristics (lower frequencies, longer link paths, quasi-linear k-R relation) and to environmental conditions (large spatial rainfall variability, convective rainfall) are considered to improve rainfall estimations across Sri Lanka. The analysis is based on data from ~1100 link paths for a 3-4 month period. The resulting rainfall depth maps are validated with multiple rain gauges across Sri Lanka at the hourly and daily time scale, and compared with space-borne weather radar data.
Until now, the majority of efforts to provide rainfall estimates from CMLs have focused on temperate climates, in Western Europe, where there generally is good coverage from weather radars and a fairly dense network of rain gauges. However, the greatest potential for this ‘opportunistic’ source of rainfall estimation lies in those regions that lack traditional surface rainfall observations, most notably low- to middle income countries, and mountainous areas, where rain gauges are scarce or poorly maintained, and weather radars are largely unavailable.
With this study we further highlight the potential for CMLs to provide high-resolution space-time rainfall observations in the tropics for use in a wide range of hydrometeorological applications, such as forecasting rainfall-induced natural hazards (flash floods, landslides) and validating satellite rainfall products.
How to cite: Walraven, B., Overeem, A., Leijnse, H., Coenders, M., Hut, R., van der Valk, L., and Uijlenhoet, R.: Considering local network characteristics and environmental conditions improves rainfall estimates from commercial microwave links in Sri Lanka, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9544, https://doi.org/10.5194/egusphere-egu22-9544, 2022.
This study evaluates OTT Parsivel2 raindrop fall speed measurements using measurements from a collocated High-speed Optical Disdrometer (HOD). Raindrop fall speed is an important quantity for calculating precipitation parameters such as raindrop kinetic energy and size distribution that are critical for various hydrological and meteorological applications. In relevant applications, raindrop fall speed has often assumed to be terminal that is typically predicted by using terminal speed – raindrop size relationships obtained from laboratory observations. Nevertheless, recent field studies have shown deviations of raindrop fall speed observations from the predicted terminal speeds; and hence, highlighted the importance of observational raindrop fall speed information. Considering the large userbase of OTT Parsivel2, this study assesses the raindrop fall speed measurements of this instrument with respect to the HOD measurements during rainfall events with a range of rainfall intensities. The results of this investigation with potential implications will be discussed in this presentation. This material is based upon work supported by the National Science Foundation under Grants No. AGS-1741250.
How to cite: Testik, F. and Saha, R.: Comparative Evaluation of OTT-Parsivel2 Measurements for Raindrop Fall Speed Using a Collocated High-speed Optical Disdrometer (HOD), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9564, https://doi.org/10.5194/egusphere-egu22-9564, 2022.
Remote sensing measurements provided by satellite-borne radars play a fundamental role in estimating precipitation distribution worldwide. However, they are subjected to a variety of potential errors and need continuous validation with ground-based measurements. Validating satellite products using measurements collected by sensors at the ground has been addressed in the literature, but it is still challenging due to intrinsic differences in the measuring principle and viewing geometries of the instrument being compared each other. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at the global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. After the launch of the GPM Core Satellite, on February 2014, an extensive Ground Validation (GV) program was established with the aim of evaluating the performance of the retrieval algorithms, over long periods and in different climatic regions across the world. Since the free availability of GPM data, many studies have been conducted to compare and validate the available version of satellite precipitation products with data collected by ground-based instrumentations such as radars and rain gauges, however very few published studies used networked disdrometers data on national scale.
For the first time, we used disdrometers to evaluate near surface GPM-DPR products (Version V06A) against long time series of measurements collected by seven laser disdrometers dislocated along the Italian peninsula and networked thanks to a cooperation effort of seven institutions (including research centers, universities and environmental regional agencies). The comparison was made in terms of rainfall and DSD parameters: rainfall rate, radar reflectivity, mass-weighted mean diameter (Dm), and normalized gamma DSD intercept (Nw). The comparison showed limited differences between single- or dual-frequency GPM algorithms, although the former presents better performance in most cases. The conclusions suggest that the agreement was good for rain rate, reflectivity factor, and Dm, while Nw satellite estimates need to be improved. Same method is used for evaluating current V07A of precipitation products.
Given the collaborative nature that has allowed the validation analysis presented, this study also represents an opportunity to consolidate cooperation between Institutions managing disdrometers in Italy and set the stage for future plans aimed at improving the use of disdrometer data in Italy.
How to cite: Adirosi, E., Porcù, F., Montopoli, M., Baldini, L., Bracci, A., Capozzi, V., Annella, C., Budillon, G., Bucchignani, E., Zollo, A. L., Cazzuli, O., Camisani, G., Bechini, R., Cremonini, R., Antonini, A., Ortolani, A., and Melani, S.: Using disdrometers data to evaluate GPM-DPR products over Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9716, https://doi.org/10.5194/egusphere-egu22-9716, 2022.
It is well known that severe weather and heavy precipitation are closely connected to the presence or passage of cold fronts over a region. In this study, the MedFTS_DT scheme, developed recently for the identification of cold fronts, is used to perform an objective climatological analysis of cold frontal activity and precipitation in the Mediterranean region. The MedFTS_DT algorithm has been developed for the automated and objective identification of cold fronts and optimized for the Mediterranean. It is based on a combination of wind-shift and thermal criteria. Wind-shift is applied in 6-hour intervals for the identification of cold fronts, whereas the thermal criteria are used to properly filter out any erroneous frontal identifications.
In this work, the spatial distribution and frequency of cold fronts are calculated over the Mediterranean for the period 2007–2016 on a monthly, seasonal and annual basis. The spatial distribution of the total precipitation (TP) and the frontal-induced precipitation (FP) are also calculated for the same region and temporal scales in order to determine the contribution of cold fronts to the total precipitation (FP/TP) It is observed that, in general, the local maxima of FP agree well with the corresponding maxima of frontal activity. It also becomes evident that, contrary to the TP regime, the maxima of FP are not found over the main mountain ranges of the Mediterranean regions, suggesting that orography does not play an important role in the formation of FP.
How to cite: Bitsa, E., Flocas, H. A., Hatzaki, M., Kouroutzoglou, J., Rudeva, I., and Simmonds, I.: Climatological study of frontal precipitation over the Mediterranean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10303, https://doi.org/10.5194/egusphere-egu22-10303, 2022.
The LIAISE (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment) field campaign was designed to study the effects of irrigation on a semi-arid area in NE Spain (Boone et al. 2019). Within the framework of LIAISE, the WISE-PreP project was conceived to examine precipitation processes, on the one hand collecting high resolution data using Parsivel disdrometers and Micro-Rain Radars complementing operational rain-gauge and C-band Doppler weather radar observations and on the other one, carrying out numerical simulations to improve our understanding of physical processes involved. In this presentation we explore the irrigation impact on precipitation in Weather Research and Forecasting (WRF) model simulations during the intensive period of the LIAISE field campaign (15-30 July 2021). We quantify the precipitation accumulation and distribution by including the irrigation parameterization (Valmassoi et al 2020) and varying its parameters (days of irrigation, amount of irrigated water, hours of irrigation, etc.). First results indicate that fractional area of precipitation is greater if the irrigation parameterization is activated and if the irrigated amount is greater as well. Finally, we explore differences in stratiform vs convective fractions of precipitation. This work was partly funded by the project “Analysis of Precipitation Processes in the Eastern Ebro Subbasin” (WISE-PreP, RTI2018-098693-B-C32, MINECO/FEDER) and the Water Research Institute (IdRA) of the University of Barcelona.
References
Boone A, Best M, Cuxart J, Polcher J, Quintana P, Bellvert J, Brooke J, Canut-Rocafort G, Price J (2019). Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE). Gewex News, February 2019.
Valmassoi A, Dudhia J, Sabatino SD, Pilla F (2020). Evaluation of three new surface irrigation parameterizations in the WRF-ARW v3. 8.1 model: the Po Valley (Italy) case study. Geoscientific Model Development, 13(7), 3179-3201.
How to cite: Bech, J., Udina, M., and Peinó, E.: Preliminary results of irrigation impact on precipitation forecasts during the LIAISE-2021 field campaign, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10647, https://doi.org/10.5194/egusphere-egu22-10647, 2022.
The precipitation type (e.g. rain, snow, hail) is of great importance in many areas such as road and aviation safety, severe weather warnings, hydrology, and agriculture. The purpose of the present research at the Royal Meteorological Institute of Belgium (RMI) is to combine the weather information from several sources in order to provide a comprehensive real-time product of the precipitation type at ground in Belgium. The preliminary results, based on observations from dual-polarization weather radars as well as on NWP model outputs, will be discussed. A comparison of these results with the crowd-sourced observations gathered through the RMI smartphone application will be shown and the perspectives for further developments will be presented.
How to cite: Watelet, S., Delobbe, L., and Reyniers, M.: Polarimetric radar observations, NWP output and crowd-sourced information for precipitation type identification in Belgium, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10690, https://doi.org/10.5194/egusphere-egu22-10690, 2022.
Space-based quantitative snowfall remote sensing has advanced in the last decade to using single-scattering properties derived from realistically shaped hydrometeors, whose value is amply demonstrated with the drastic improvement in the consistency between active and passive retrievals and has benefited the active-passive combined algorithm of NASA’s Global Precipitation Measurement mission. However, the two principal processes required to obtain these scattering properties, i.e. the numerical generation of synthetic hydrometeors with realistic morphology and the subsequent solution of the electromagnetic scattering problem, are orders of magnitude more demanding in computation and storage resources than those required for hydrometeors with shapes based on simplifying assumptions. Recent evidence from microwave radiometer polarization signals suggests that uniform random orientation of the hydrometeors in solid precipitation is rarely a valid assumption. Axially symmetric scattering properties derived from orientation averages, in turn, rarely apply. The consistent quantitative physical retrieval of snowfall now calls for polarimetric and orientation-dependent scattering properties. Due to the general lack of symmetry for the solid hydrometeors, each orientation has a unique scattering solution. Since hundreds of orientations may be needed for each particle the storage demand grows proportionally. This challenge is not unique to snowfall remote sensing. Space-based quantitative remote sensing of cloud ice and aerosol face similar problems, for the particles of concern in these applications are mostly non-spherical and complexly shaped as well. Furthermore, heterogeneous composition of the pertinent particles, such as melting hydrometeors, hydrometeors with pollutant enclosures, and mixed composition aerosol or dust particles, further exacerbates the problem. For example, as we attempt to deal with the nearly ubiquitous melting layers in precipitation systems, we have discovered that, since the solid hydrometeors in the melting layer may likely be at different stages of melting, we must consider a range of liquid mass fraction for each solid hydrometeor. We thus need tens of melting instances at different liquid mass fractions with their associated scattering properties, ballooning the resource requirement further by ~10 fold! We, as a community in particulate matter remote sensing, can ill afford to repeat such computationally intensive electromagnetic scattering calculations or duplicate the needed storage for storing their results. We must find a strategy to sustainably enhance the long-term availability, accessibility, and usability of these valuable data. For such a purpose, we need first a more suitable and better designed means than “data files” to warehouse the realistic hydrometeor structures along with associated single-scattering properties and second a flexible and extensible means to disseminate the warehoused data. Both of these must also be scalable and performant. In terms of technological choices, we recommend employing a parallel (distributed) database management system for warehousing with web services enabled for access and dissemination. We believe the data centers and services of NASA through its Earth Science Data Systems program provide the best long-term solution to this challenge.
How to cite: Kuo, K.-S., Adams, I., Olson, W., Fenni, I., Pelissier, C., Schrom, R., Loftus, A., Clune, T., and Braun, S.: Warehousing and Disseminating Single-Scattering Properties for Particulate Matter Remote Sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10714, https://doi.org/10.5194/egusphere-egu22-10714, 2022.
Large errors in Quantitative Precipitation Estimates (QPE) tied to remote-sensing retrieval algorithms remain a challenge especially in complex terrain with fast hydrologic response. We propose a new framework to derive dynamic hydrologic corrections of rainfall in headwater basins that constrains water budget closure at a desired time-scale and distributes transient rainfall corrections along runoff trajectories by Lagrangian backtracking constrained by realistic time-of-travel distributions. Downscaled QPE products (250 m resolution) are applied first as input to a distributed hydrologic model to predict runoff trajectories and the event hydrograph at the basin's outlet. Second, time-varying rainfall corrections are derived from the residuals between predicted and observed discharge at the outlet. Finally, the corrections are spatially distributed following the runoff trajectories backward (i.e. trajectories are used as streaklines originating at the basin's outlet). Because nonlinear interactions between rainfall, runoff and storage are transient, the corrections are applied recursively until the shape and volume of the predicted hydrograph are stable. The framework is applied to ground-based (e.g. Stage IV) and satellite-based remote-sensing QPE (e.g. IMERG) associated with the 49 largest floods 2008-2018 in the Southern Appalachian Mountains, USA. The results show improvements in hydrograph prediction efficiency skill at 15min timescale from -0.5 to 0.6 on average and up to 0.9 for warm season events, bounding event runoff volume errors with a mean of 3%, and reducing time to peak errors by half an hour on average. Corrected QPE exhibits nearly perfect correlation and no bias at high elevation gauge locations. Cumulative uncertainty in the water budget closure at event scale is less than the uncertainty in streamflow measurements. Error attribution shows strong organization of QPE corrections according to seasonal weather and rainfall regime, thus providing a path to generalization to ungauged mountain basins.
How to cite: Liao, M. and Barros, A.: Toward Optimal Rainfall – Hydrologic QPE Correction in Headwater Basins, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10845, https://doi.org/10.5194/egusphere-egu22-10845, 2022.
The main advantages of commercial microwave links (CMLs) as opportunistic rainfall sensors are their availability even in sparsely gauged regions and their close-to-ground rainfall observations. Moreover, the observations are accessible with a delay of only several seconds within cellular telecommunication networks. However, this access is in practice constrained by legal and administrative burdens. CML rainfall research suffers from this general limitation and proposed methods have thus not been developed and evaluated for different datasets across the boundaries of institutions and countries (Chwala and Kunstmann, 2019). Due to the fact that CML data is typically acquired on a national level and not openly shared, the exchange of data, the cross-validation of methods and transboundary applications of CML data have not been realized up until now.
In the proposed study we process large CML data sets from Germany and the Czech Republic and, for the first time, generate transboundary rainfall maps. We work with unique data sets from two independent data acquisition systems which are successfully merged into one rainfall product. The CML product covers the whole of Germany and the western part of Czech Republic including border mountain regions where radar products are affected by ground clutter and rain gauge networks are sparse. We analyze 1-min observations of 4000 CMLs in Germany and 2500 CMLs in the Czech Republic during summer period 2021, which contains periods of heavy rainfalls as well as clear dry-weather intervals. The resulting rainfall maps are compared with gauge and radar observations.
The results of the study provide the evidence that CML rainfall retrieval in transboundary or continental scale is applicable. The generated rainfall maps from opportunistic sensing are of high quality and can be further used for assimilation with other data sources. We also demonstrate that the interoperability of CML data sets is possible which was one of the largest deficits up to today. Generation of transboundary rainfall maps represents an important milestone on a way to the CML-based operational rainfall product at continental scale.
Chwala, C. and Kunstmann, H. (2019) Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges. WIREs Water. 6:e1337. https://doi.org/10.1002/wat2.1337.
This study was supported by the bilateral project SpraiLINK of the German Research Foundation (432287169) and the Czech Science Foundation (20-14151J).
How to cite: Bareš, V., Chwala, C., Fencl, M., Blettner, N., and Špačková, A.: Czech-German transboundary rainfall fields generated from two independent networks of commercial microwave links, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11292, https://doi.org/10.5194/egusphere-egu22-11292, 2022.
Large cities are especially vulnerable to heavy precipitation events, which can lead to significant economic losses. This topic is relevant both due to the observed increase in the frequency of dangerous weather phenomena (including extreme precipitation [Ye et al., 2017; Chernokulsky et al., 2019]) in midlatitudes in general, and due to the previously noted facts of intensification of deep atmospheric convection and associated rainfall over urban areas [Han et al., 2014; Liu, Niyogi, 2019]. Yet, despite the numerous studies, the magnitude of urban effects on intense precipitation and their physical drivers are not fully understood.
In this study, we investigate urban effects on intensity and frequency of summer precipitation events exemplified by Moscow megacity, Russia. Previously, increase of mean summer precipitation amount by 10% over Moscow was revealed according to COSMO-CLM simulations for multiyear period [Varentsov et al., 2018]. Here we use long-term (1988-2021) observations at urban and rural weather stations. Statistical analysis is performed separately for categories of precipitation intensity. Moreover, using ERA5 reanalysis data [Hersbach et al, 2020] we estimated atmospheric convective instability and frontal parameters in order to classify precipitation cases according to the synoptic situation. This will help us to understand the physical mechanisms of precipitation intensification better. The assumption is that megacity influence on frontal systems is less noticeable than its influence on local convective clouds and convective systems in the low pressure gradient filed. Also we collected a catalogue of extreme precipitation cases in Moscow region exceeding 0.999 quantile values and studied most interesting cases among them.
Eventually, we obtained qualitative and quantitative estimates of the Moscow impact on the characteristics of intense precipitation for various synoptic conditions.
Acknowledgements:
The study was supported by the Russian Ministry of Science and Higher Education (grant of President of Russian Federation for young PhD scientists No. МК-5988.2021.1.5, agreement No. 2020-220-08-5835).
References:
Chernokulsky, A., Kozlov, F., Zolina, O., Bulygina, O., Mokhov, I. I., & Semenov, V. A. (2019). Observed changes in convective and stratiform precipitation in Northern Eurasia over the last five decades. Environmental Research Letters, 14(4), 045001.
Han, J. Y., Baik, J. J., & Lee, H. (2014). Urban impacts on precipitation. Asia-Pacific Journal of Atmospheric Sciences, 50(1), 17-30.
Hersbach, H. et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049.
Liu, J., & Niyogi, D. (2019). Meta-analysis of urbanization impact on rainfall modification. Scientific reports, 9(1), 1-14.
Varentsov M., Wouters H., Platonov V., & Konstantinov P. (2018). Megacity-Induced Mesoclimatic Effects in the Lower Atmosphere: A Modeling Study for Multiple Summers over Moscow, Russia. Atmosphere, 9(2), 50.
Ye, H., Fetzer, E. J., Wong, S., & Lambrigtsen, B. H. (2017). Rapid decadal convective precipitation increase over Eurasia during the last three decades of the 20th century. Science advances, 3(1), e1600944.
How to cite: Yarinich, Y., Varentsov, M., Platonov, V., and Stepanenko, V.: Impact of Moscow city on intense summer precipitation: statistical analysis based on long-term observations , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11614, https://doi.org/10.5194/egusphere-egu22-11614, 2022.
In this study, the temperature lapse rate (TLR) was estimated for the Uttarakhand region using MODIS-LST (day and night) and the observed air temperature data extracted from the 107 stations. The objective of this study is to estimate the TLR: first directly from MODIS-LST referred as “TLR_DFM” and the second from the observed station data which is referred as “TLR_St.”. The result of our analysis shows that MODIS-LST estimated TLR during the day-time (-0.54°C/100 m) is more accurate than the night-time (-0.59°C/100 m) and shallower during the winter months than summer months. The spatial and temporal changes of TLR from 107 stations varies significantly with steepest summer and shallowest winter ranging from -0.12 °C/100 m to -1.1°C/100 m of maximum, minimum and mean temperature. The highest TLR occur in June of maximum temperature, while the lowest occur in December of minimum temperature. This observation contradicts with standard temperature lapse rate (-0.65°C/100 m) which is used globally for most of the ecological and hydrological models. Further, for the validation of performance the time series LST data derived from the satellite were correlated with the observed air temperature data for a complete 1 year (2020). Thus, the results found out to be highly correlated, that the TLR for the exact pixel has a great potential than the observed air temperature in extremely sparse region. This study further helps in understanding the results of various land surface process related to climatology, hydrology where the use of standard temperature lapse rate (STLR) is an essential input in the high mountainous region.
How to cite: Negi, P., Goswami, A., and Joshi, G. C.: Estimation of Temperature Lapse Rate Techniques over Uttarakhand Region, Western Himalaya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11840, https://doi.org/10.5194/egusphere-egu22-11840, 2022.
Quantitative estimation of snowfall using radar is a challenging task that is usually accomplished using relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR), typically expressed as power-law (Ze = a × SRb) whose parameters are obtained from long-term measurements. Unfortunately, the changeability of microphysical and scattering characteristics of snowflakes makes them highly variable. The proposed method takes advantage of the estimation of the snowflake microphysical characteristics and develops six Ze-SR relationships depending on particle habit. A classification of particles is obtained by comparing co-located Micro Rain Radar and Parsivel disdrometer observations coupled with a DDA backscattering model in terms of radar reflectivity and is used to select the appropriate Ze-SR relationship.
The method was tested using ground-based instruments installed at the Italian Antarctic Station Mario Zucchelli, in the framework of the projects APP (Antarctic Precipitation Properties), MALOX (MAss LOst in wind fluX), and IAMCO (Italian Antarctic Meteo-Climatological Observatory), funded by the Italian National Antarctic Program (PNRA).
The Micro Rain Radar was set at the highest vertical resolution (35 m) so that the first trusted range gate was at only 105-m height, close enough to the ground level to be compared with disdrometer particle size distributions. We analyzed data from 52 precipitation days of the 2018–2019 and 2019–2020 summers for a total of 23,566 snowfall minutes.
Disdrometer data were corrected from the influence of wind by assigning a reliability weight to each Parsivel bin based on simultaneous disdrometer, MRR, and wind measurements. This method preserves more precipitation data than the more widely used censoring methods that eliminate data collected when wind speed exceeds a given threshold: since strong winds are often associated with significant snow events, censoring methods tend to discard inportant precipitation measurements.
The consistency of disdrometer and radar measurements is tested for six snow categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine) in terms of radar reflectivity matched in a 10-min time frame. The related Ze-SR relationship of the selected snow category is used to calculate the cumulated snowfall amount.
The comparisons of Ze from disdrometer and MRR at the 105-m height show good agreement, even for nonwind-corrected disdrometere data, although agreement significantly improves if wind-correction is applied.
Of the precipitation minutes, we classified 75% of them as aggregate, with a significant percentage of dendrites. Only 5,830 out of 23,566 falling particles showed pristine characteristics. We estimated 84.6 mm w.e. of accumulated snowfall for the 52 events. Such estimates were compared with measurements from a weighing pluviometer available for 32 out of the 52 considered days. Estimation using variable Ze-SR relationships results in a better agreement with the pluviometer (64 mm w.e. vs. 66.5 mm w.e.) with respect to estimates from fixed Ze-SR relationships found in the literature.
Results show that combining MRR and disdrometer is undoubtedly valuable for snowfall estimations. In fact, the significant uncertainties in snowfall radar estimates related to the variability of snow microphysical features can be mitigated by using variable Ze-SR relationships.
How to cite: Bracci, A., Baldini, L., Roberto, N., Adirosi, E., Montopoli, M., Scarchilli, C., Grigioni, P., Ciardini, V., Levizzani, V., and Porcù, F.: Using variable relationships between reflectivity and snowfall rate obtained from coincident MRR and disdrometer measurements to estimate snowfall at Mario Zucchelli Antarctic Station, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12202, https://doi.org/10.5194/egusphere-egu22-12202, 2022.
Drought is usually reported as the phenomenon of rainfall deficiency, compared to its long-term mean, that impacts a large area over a specific time period. It involves features in several dimensions, as it starts imperceptibly, advances slowly and cumulatively, and its consequences show up gradually. Researchers usually work with climatic indices and parameters for drought monitoring, but as this phenomenon is related to multiple characteristics, such methods are not enough to estimate the temporal and spatial drought elements.
Cyprus, located in the Southeast Mediterranean basin, faces climatic extremes due to the climate crisis, and particularly precipitation decrease. Due to its semi-arid to arid climate, Cyprus is significantly vulnerable to droughts. The island experiences frequent droughts that result in various problems to the environment, the economy, and the agricultural production.
Various sources in literature provide analysis and review of drought occurrence in Cyprus, but the applied methods are limited to in-situ monitoring, involving mainly precipitation and temperature parameters from meteorological stations. The dependence of drought monitoring solely on in-situ data constitutes a significant risk for decision makers and stakeholders, as in case of technical damages, or remote areas of interest, drought monitoring will be insufficient or even impossible. Remotely sensed data yield continuous, digital and spatially explicit information on earth’s processes around the globe and present an essential tool in overcoming the aforementioned risk.
In the context of this study, observations from NASA’s Global Precipitation Measurement (GPM) mission are used to calibrate the data from the two ground-based radars of the Department of Meteorology (DoM). The DPR (Dual-frequency Precipitation Radar) aboard of GPM is employed in order to derive the reflectivity and the respective precipitation rate at the ground with a spatial resolution of 5-25km for 120km wide swath. The ground-based radars scan in PPI mode with the radar holding an elevation angle constant and varying its azimuth angle and provide raw information with a spatial resolution of 0.1° and a radius of 150km. The radar stations are located in Rizoelia, Larnaca district and Nata, Paphos district.
This presentation will demonstrate the quantitative precipitation rate maps, as well as the precipitation classification maps that are produced using the calibrated precipitation datasets. The results will contribute to the estimation of the precipitation budget and distribution over the area of Cyprus and thus, drought monitoring in the broader area.
The presented work is developed under the auspices of the activities of the ‘ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment’- ‘EXCELSIOR’ project that 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: Loulli, E., Bühl, J., Michaelides, S., Loukas, A., and G. Hadjimitsis, D.: Precipitation classification and quantitative mapping using ground-based radar data, intended for drought monitoring in Cyprus, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12427, https://doi.org/10.5194/egusphere-egu22-12427, 2022.
Commercial Microwave Link (CML) data can provide important rainfall information, in particular in regions with low density of rain gauges and with no radar coverage. We have set up and operate a CML data acquisition (DAQ) system for Burkina Faso and report on the first larger scale analysis of the derived rainfall information.
Our real-time DAQ system started as a pilot project covering only eight CMLs and was gradually extended. For the monsoon season 2020 and 2021 it collected data for more than 1000 CMLs in Burkina Faso with a temporal resolution of one minute. Our first analysis is focusing on the 300 CMLs which operate in the frequency range between 11 GHz and 13 GHz in and around the city of Ouagadougou, the capital of Burkina Faso. We carry out a comparison with official daily rain gauge data, both for individual CMLs as well as for CML-derived rainfall maps. Our results for the period of the 2019, 2020 and 2021 rainy season indicate good performance of the CML rainfall information, with a Pearson correlation coefficient of 0.8 and higher.
The processing of the longer CMLs in the frequency range between 7 GHz and 9 GHz, which connect the urban centers in Burkina Faso, currently is in progress. To tackle the challenge of noisy dry periods we are investigating the use of cloud cover and cloud type information from MSG SEVIRI data.
How to cite: Djibo, M., Chwala, C., Doumounia, A., Polz, J., Graf, M., Ouédraogo, W. Y. S. B., Sanou, S. R., Sawadogo, M., Guira, I., Kunstmann, H., and Zougmoré, F.: Improving rainfall monitoring using commercial microwave link data in Burkina Faso: Results from three years of processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12445, https://doi.org/10.5194/egusphere-egu22-12445, 2022.
Mediterranean hurricanes (Medicanes) are meso-scale cyclones typical of the Mediterranean area which during their lifetime may some some dynamical features with tropical cyclones: the presence of a quasi-cloud-free calm eye, spiral-like cloud bands elongated from the center, strong winds close to the vortex center and a deep warm core. They are often associated to heavy rainfall and flooding, intense wind, and high waves and storm surge, and can be serious threats to human life and infrastructure. Recent studies highlighted that extra-tropical and tropical-like cyclone characteristics can alternate or even coexist in the same cyclonic system, and that only in some cases strong diabatic forcing leads to tropical-like transition (i.e., purely barotropic structure). In this study the satellite measurements from the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) active and passive microwave (MW) sensors are used to analyze the precipitation structure of the most intense Mediterranean Hurricane (Medicane) on record, named Ianos, which swept across the Ionian Sea between 14 and 18 September 2020. Two GPM-CO overpasses, available during Ianos development and tropical-like cyclone (mature) phase, are analyzed in detail. GPM Microwave Imager (GMI) measurements are used to carry out a comparative analysis of the medicane precipitation structure and microphysics processes between the two phases. The GPM-CO Dual-frequency Precipitation Radar (DPR) overpass, available for the first time during a medicane mature phase, provides key measurements and products to analyze the 3D precipitation structure in the rainbands, offering further evidence of the main precipitation microphysics processes inferred from the passive MW measurement analysis. Substantial difference in the rainband precipitation structure is observed, with deeper convection and stronger updraft features during development then at the mature phase, when also shallow precipitation/warm rain processes are observed in the inner region around the medicane eye. These features play a key role to explain the substantial drop in lightning activity during Ianos mature phase. Graupel-ice electrification process is inhibited due to the combined effect of strong horizontal wind and the observed limited growth of graupel. Starting from the detailed analysis of Ianos, a comparative study among the medicanes occurred during the GPM era is carried out. The goal is to extract common features from PMW measurements characterizing the different stage of medicanes’ evolution. The study demonstrates the value of the GPM-CO not only to characterize medicane precipitation structure and microphysics processes and convection strength with unprecedented detail, but also to provide evidence of tropical-like characteristics and of similarities with tropical cyclones for those medicanes undergoing tropical-like transition during their mature phase.
How to cite: D'Adderio, L. P., Casella, D., Dietrich, S., Rysman, J.-F., Sanò, P., and Panegrossi, G.: Unprecedented observations of Medicane precipitation structure from the GPM Core Observatory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12649, https://doi.org/10.5194/egusphere-egu22-12649, 2022.
The economy of West Africa largely relies on rain-fed agriculture, making economic growth and food security in this region highly dependent on rainfall and the knowledge of rainfall patterns. However, accurate rainfall information is currently missing due to a sparse rain gauge distribution and challenges in data transmission. Satellites could offer a solution, but existing products show poor correlation with rain gauge data. Possible reasons for this are the specific atmospheric characteristics of West Africa and rainfall processes still not fully understood.
To address this challenge, a new satellite rainfall product has been developed at TU Delft, within the Schools and Satellites (SaS) CSEOL project, funded by the European Space Agency through the IHE Institute for Water Education. SaS had the goal of producing reliable rainfall information for West Africa by combining Earth Observation, Deep Learning (DL) and Citizen Science. The focus area was the North of Ghana. The resulting product, RainRunner, performs rainfall detection at a 3-hour temporal and 0.03◦ x 0.03◦ spatial resolution, based only on TIR Meteosat Second Generation data. Two DL architectures have been designed: one using only Convolutional Neural Networks (CNN) and another one featuring a Convolutional Long Short-Term Memory layer before a CNN architecture. We have also introduced a methodology to train DL models when accurate high-density data are missing on the ground, that employs point-based instead of gridded rainfall data. RainRunner uses rain gauge data from the Trans-African Hydro-Meteorological Observation (TAHMO) as target data. A secondary validation with daily manual rain gauge data gathered by the SaS Citizen Observatory in the North of Ghana demonstrated that RainRunner has a remarkable generalization ability.
We will show that RainRunner achieves performance very close to that of Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and outperforms the well-established Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System (PERSIANN-CCS). Advantages of RainRunner are that it is fully data-driven, simpler to deploy, operating on a regional scale in quasi real-time, i.e., it can be applied as soon as GEO IR images become available.
This work illustrates the potential of DL for satellite rainfall retrieval in a data scarce context. To the best knowledge of the authors, this is the first study in which a DL-based rainfall detection model is trained locally over a region in Africa. This work could set a stage towards better rainfall information in areas of the world where it is currently missing, ultimately contributing to climate adaption worldwide.
How to cite: Estebanez Camarena, M., Taormina, R., van de Giesen, N., and ten Veldhuis, M.-C.: Deep Learning for rainfall detection in a data scarce context: an application to the Sahelian Savanna, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12847, https://doi.org/10.5194/egusphere-egu22-12847, 2022.
