Precipitation Retrieval Research Articles

The state of Earth Observing system today: updates compiled from recent EUMETSAT Meteorological Satellite Conferences

Abstract This work presents highlights from the 2021 and 2022 EUMETSAT conferences, drawn from their presentations, posters and panel discussions and placed into the wider context of the global Earth Observing (EO) system. These highlights collectively reveal much about the current state of knowledge about the EO system, its potential future evolutions and how that system may be used to produce useful products and services. European, American and Asian space agencies presented their visions for the next generation of operational satellite programmes and demonstrated how these will continue to improve environmental forecasting and monitoring products. User communities presented updates in the use of satellite data, including climate records, novel precipitation retrievals, drought monitoring, weather forecasting and retrievals of a broadening range of trace gases. On the technology side, discussion on the impact of Artificial Intelligence (AI) for Earth observation was a major theme, particularly for weather forecasting, data assimilation or other environmental predictions. Cloud computing was another topic due to its potential to streamline the workflow of EO scientists, enhancing collaborations and unlocking access to previously-unavailable data or computing resources. Finally, discussion on the miniaturization of observational instruments was another major theme of both conferences, highlighting both the possibility of novel or enhanced observations and the emerging economic case for commercial entities to operate fleets of meteorological satellites.

The Performance of GPM IMERG Product Validated on Hourly Observations over Land Areas of Northern Hemisphere from Global Meteorological Stations

The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of IMERG data needs to be validated, as this technology is essentially an indirect way to obtain precipitation information. This study evaluated the performance of IMERG final run (version 6.0) products from 2001 to 2020, using three sets of gauge-derived precipitation data obtained from the Integrated Surface Database, China Meteorological Administration, and U.S. Climate Reference Network. The results showed a basic consistency in the spatial pattern of annual precipitation total between IMERG data and gauge observations. The highest and lowest correlations between IMERG data and gauge observations were obtained in North Asia (0.373, p < 0.05) and Europe (0.308, p < 0.05), respectively. IMERG data could capture the bimodal structure of diurnal precipitation in South Asia but overestimates a small variation in North Asia. The disparity was attributed to the frequency overestimation but intensity underestimation in satellite inversion, since small raindrops may evaporate before arriving at the ground but can be identified by remote sensors. IMERG data also showed similar patterns of interannual precipitation variability to gauge observation, while overestimating the proportion of annual precipitation hours by 2.5% in North America, and 2.0% in North Asia. These findings deepen our understanding of the capabilities of the IMERG product to estimate precipitation at the hourly scale, and can be further applied to improve satellite precipitation retrieval.

Precipitation Retrieval from FY-3G/MWRI-RM Based on SMOTE-LGBM

Using the FY-3G/MWRI-RM observations, this paper proposes a precipitation retrieval method that combines the Synthetic Minority Over-sampling Technique with Light Gradient Boosting Machine (SMOTE-LGBM) and analyzes the impact of MWRI-RM channel settings on precipitation retrieval. The SMOTE-LGBM-based model consists of two LGBM models for precipitation identification and estimation, respectively. The SMOTE method is used to address the imbalance between precipitation and non-precipitation samples. Using the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product as a reference, we validate the retrieved precipitation by the SMOTE-LGBM-based model with an independent testing dataset. The critical success indexes are 0.483 and 0.526, and the Pearson correlation coefficients are 0.611 and 0.645 for the ocean and land regions, respectively. The spatial distributions of the retrieved and IMERG accumulated precipitation in the testing dataset are similar. In addition, we visualize and analyze the cases of Meiyu and two typhoons. The results indicate that the SMOTE-LGBM-based model effectively represents the spatial distribution characteristics of precipitation and achieves high agreement with IMERG precipitation products. Overall, the SMOTE-LGBM-based model successfully retrieves precipitation from MWRI-RM and provides accurate precipitation products for FY-3G/MWRI-RM for the first time.

Evaluating hydrological responses of satellite precipitation products over an Indian tropical catchment through a distributed physical model

AbstractSignificant advancements in satellite‐based precipitation retrieval algorithms have led to the development of continuous, quasi‐global precipitation products, offering unique opportunities for hydrometeorological and climate research. In this context, six satellite‐based precipitation products (SPPs), including CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were thoroughly investigated for their application in hydrological simulations over the Wardha River basin in India. The observed gridded precipitation product developed by India Meteorological Department (IMD) has been used as reference data to evaluate the performance of SPPs. Hydrological variables such as runoff, soil moisture (SM) and evapotranspiration (ET) were simulated using the Variable Infiltration Capacity model. The performances of SPPs are critically assessed using various statistical metrics, including Pearson's correlation coefficient (R), Klinga‐Gupta Efficiency (KGE), Percent Bias (PBIAS), root mean square error (RMSE), and the RMSE to standard deviation ratio (RSR). The model‐simulated discharge was compared with streamflow observations at a single gauging site, while a spatially distributed comparison was conducted between simulated SM and ET and satellite‐based SM and ET. The IMD dataset consistently shows superior performance for discharge simulation at both daily and monthly scales, with KGE values of 0.85 and 0.91, respectively. Among SPPs, MSWEP and CHIRPS excel in simulating daily and monthly discharge, respectively. For ET simulation, CHIRPS outperforms IMD and other SPPs, achieving an overall KGE value of 0.37. In contrast, PERSIANN is the most effective for simulating SM compared to other precipitation products.

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data

Abstract Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostly novel even for the more general task of quantifying predictive uncertainty in spatial prediction settings. On 15 years of monthly data from over the contiguous United States (CONUS), we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). Their ability to issue predictive precipitation quantiles at nine quantile levels (0.025, 0.050, 0.100, 0.250, 0.500, 0.750, 0.900, 0.950, 0.975), approximating the full probability distribution, was evaluated using quantile scoring functions and the quantile scoring rule. Predictors at a site were nearby values from two satellite precipitation retrievals, namely PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals), and the site’s elevation. The dependent variable was the monthly mean gauge precipitation. With respect to QR, LightGBM showed improved performance in terms of the quantile scoring rule by 11.10%, also surpassing QRF (7.96%), GRF (7.44%), GBM (4.64%) and QRNN (1.73%). Notably, LightGBM outperformed all random forest variants, the current standard in spatial prediction with machine learning. To conclude, we propose a suite of machine learning algorithms for estimating uncertainty in spatial data prediction, supported with a formal evaluation framework based on scoring functions and scoring rules.

Has IMERG_V07 Improved the Precision of Precipitation Retrieval in Mainland China Compared to IMERG_V06?

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG.

AttUnet_R_SFT: A Novel Network to Explore the Application of Complex Terrain Information in Satellite Precipitation Estimating

AbstractAccurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research.

A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package

AbstractMicrophysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest

Precipitation is the basic component of the Earth’s water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI.

An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products

Abstract. The objective of the Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) mission is to infer attributes of cloud, aerosol, precipitation, and radiation from observations made by four complementary instruments. This requires the development of single-instrument and multiple-instrument (i.e. synergistic) retrieval algorithms that employ measurements made by one, or more, of EarthCARE's cloud-profiling radar (CPR), atmospheric lidar (ATLID), and multi-spectral imager (MSI); its broadband radiometer (BBR) places the retrieved quantities in the context of the surface–atmosphere radiation budget. To facilitate the development and evaluation of ESA's EarthCARE production model prior to launch, sophisticated instrument simulators were developed to produce realistic synthetic EarthCARE measurements for simulated conditions provided by cloud-resolving models. While acknowledging that the physical and radiative representations of cloud, aerosol, and precipitation in the test scenes are based on numerical models, the opportunity to perform detailed evaluations wherein the “truth” is known provides insights into the performance of EarthCARE's instruments and retrieval algorithms. This level of omniscience will not be available for the evaluation of in-flight EarthCARE retrieval products, even during validation activities coordinated with ground-based and airborne measurements. In this study, we compare EarthCARE retrieval products both statistically across all simulated scenes and from a specific time series from a single scene. For ice clouds, it is shown that retrieved profiles of ice water content and effective particle size made by the ATLID-CPR-MSI cloud, aerosols, and precipitation (ACM-CAP) synergistic algorithm are consistently more accurate than those from its single-instrument counterparts. While liquid clouds are often difficult to detect from satellite-borne sensors, especially for multi-layered clouds, ACM-CAP benefits from combined constraints from lidar backscatter, solar radiances, and radar-path-integrated attenuation but still exhibits non-trivial random error. For precipitation retrievals, the CPR cloud and precipitation product (C-CLD) and ACM-CAP have a similar performance when well-constrained by CPR measurements. The greatest differences are in coverage, with ACM-CAP reporting retrievals in the melting layer, and in heavy precipitation, where CPR signals are dominated by multiple scattering and attenuation. Aerosol retrievals from ATLID compensate for a high degree of measurement noise in a number of ways, with the ATLID extinction, backscatter, and depolarisation (A-EBD) product and ACM-CAP demonstrating similar performance. The multi-spectral imager (MSI) cloud optical properties (M-COP) product performs very well for unambiguous cloud layers. Similarly, the MSI aerosol optical thickness (M-AOT) product performs well when radiances are unaffected by cloud, but both products provide little information about vertical profiles of properties. Finally, a summary of the performance of all retrieval products and their random errors is provided.

GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean

Abstract. The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals.

Infrared Precipitation Retrieval Method Based on Residual Deep Forest

Infrared Precipitation Retrieval Method Based on Residual Deep Forest

Estimation of Lightning Flash Rate in Precipitation Features by Applying Shallow AI Neural Network Models to the GMI Passive Microwave Brightness Temperatures

AbstractThe satellite‐constellation passive‐microwave Brightness Temperature (TB) observations, with global coverage, and more additions from upcoming CubeSats, have been mainly used in surface precipitation retrievals. However, these observations can also be used to indicate the intensity of convective systems. This study attempts to relate Global Precipitation Mission (GPM) Microwave Imager (GMI) TBs to Geostationary Lightning Mapper (GLM) lightning flashes by using 4 years (02/2018–04/2022) of GPM Precipitation Feature (PF) database. GMI TBs are collocated to the GLM lightning counts, and to the ERA5 reanalysis 2‐m air temperature in PFs. Three Artificial Intelligent Neural Network Models (AI‐NN) are trained to classify PFs producing lightning in a 20‐min window respectively for land, ocean, or coast. The flash rate of the determined Lightning producing PFs (LPFs) is then quantified by three other AI‐NNs, each trained for one of the three regions. Though the models clearly capture the global geographical distribution of LPFs with a Probability Of Detection over 90%, high False Alarm Rates are found, ranging from 49.9% over land to 91.5% over the ocean. The importance of TB at each passive microwave channel varies regionally, corresponding to the different microphysical properties in various types of precipitation systems. The global lightning distribution is derived by applying the AI models to global PFs and is well compared to the lightning climatology from Lightning Imaging Sensor and Optical Transient Detector. This suggests that the use of passive microwave TBs can help to fill the gaps in lightning monitoring thanks to their global coverage.

Precipitation Vertical Structure Characterization: A Feature-Based Approach

Abstract The three-dimensional (3D) structure of precipitation systems is highly dependent on hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural-network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes intercluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo-top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes. Significance Statement This study proposes a systematic model to identify precipitation profiles of distinct vertical structures and evaluate their dependence on environmental conditions. The model was developed using ground-based radar observations; however, there is potential to extend this model to reflectivity profiles from both ground- and satellite-based sensors. In addition, the identified precipitation regime clusters could be a proxy for the vertical structure of precipitation systems and assist in determining the structural variability within traditional precipitation type classification (e.g., convective versus stratiform). Moreover, identifying the precipitation regimes could also be used to improve satellite-based precipitation retrievals. Finally, a better understanding of precipitation structure would also help improve the initialization of climate models.

The State of Precipitation Measurements at Mid-to-High Latitudes

The measurement of global precipitation is important for quantifying and understanding the Earth’s systems. While gauges form the basis of conventional measurements, global measurements are only truly possible using satellite observations. Over the last 50–60 years, satellite systems have evolved to provide a comprehensive suite of observing systems, including many sensors that are capable of precipitation retrievals. While much progress has been made in developing and implementing precipitation retrieval schemes, many techniques have concentrated upon retrievals over regions with well-defined precipitation systems, such as the tropics. At higher latitudes, such retrieval schemes are less successful in providing accurate and consistent precipitation estimates, especially due to the large diversity of precipitation regimes. Furthermore, the increasing dominance of snowfall at higher latitudes imposes a number of challenges that require further, urgent work. This paper reviews the state of the current observations and retrieval schemes, highlighting the key factors that need to be addressed to improve the estimation and measurement of precipitation at mid-to-high latitudes.

AI-based estimation of precipitation in the Tibetan Plateau using multi-frame FY-4A satellite data

ABSTRACT The weather system over the Tibetan Plateau can cause disasters around the plateau and its downstream areas. Since there are few surface meteorological observation stations over the Tibetan Plateau, the precipitation here cannot be comprehensively detected. Therefore, satellite precipitation estimation products with high timeliness, accuracy, and full coverage are relied upon. This paper presents an AI-based precipitation estimation method using multi-frame FY-4A meteorological satellite data (FY-4A PRE-M). Firstly, multiple-derived features are constructed according to the brightness temperature of the satellite different channels, and the regional precipitation estimation model is then trained using the light gradient boosting machine (LGBM) algorithm. Based on the research results from precipitation retrieval using the infrared channel radiation features, this model analyzes the possible contribution of the radiation difference between channels at the same time and the radiation change in the same channel at different periods (15, 30, 45 and 60 min) to the precipitation estimation. The algorithm continuously updates the model with the history of available precipitation data. From 1 September 2021 to 31 August 2022, the precipitation estimations using the multi-frame FY-4A satellite data were evaluated through the ground rainfall gauge data based on the spatial – temporal matching. The results show that the multi-frame FY-4A satellite data have advantages in precipitation estimation. The heavy rainfall caused by a Tibetan Plateau shear line is evaluated using this algorithm. The results show that the precipitation estimations of FY-4A PRE-M and IMERG-L are close to the values of rain gauges. The FY-4A PRE-M can well describe the diurnal variations and the distribution of 24 h cumulative precipitation over the Tibetan Plateau, and the heavy rainfall belts in the central and northern Tibetan Plateau are also well monitored.

Advances in Precipitation Retrieval and Applications from Low-Earth-Orbiting Satellite Information

Advances in Precipitation Retrieval and Applications from Low-Earth-Orbiting Satellite Information

Implications for Validation of IMERG Satellite Precipitation in a Complex Mountainous Region

Satellite-based precipitation retrievals such as the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), provide alternative data in mountainous regions. In this study, we evaluated IMERG in the Yarlung Tsangbo Grand Canyon (YGC) using ground observations. It was found that IMERG underestimated the total rainfall primarily due to under-detection of rainfall events, with misses being more prevalent than false alarms. We analyzed the relationships between the probability of detection (POD), false alarm ratio (FAR), bias in detection (BID), and Heidke skill score (HSS) and terrain factors. It was found that the POD decreased with elevation, leading to increased underestimation of rainfall events at higher elevations, and the FAR was higher in valley sites. In terms of the hit events, IMERG overestimated the light rainfall events and underestimated the heavy rainfall events and the negative bias in the hit events decreased with elevation. IMERG could capture the early morning peak precipitation in the YGC region but underestimated the amplitude of the diurnal variation. This bias was inherent at the sensor level, and the Global Precipitation Climatology Center (GPCC) calibration partially improved the underestimation. However, this improvement was not sufficient for the YGC region. This study fills the gap in IMERG validation in a complex mountainous region and has implications for users and developers.

Evaluating and Improving TROPICS Millimeter‐Wave Sounder's Precipitation Estimate Over Ocean

AbstractThis paper examines precipitation retrievals from Kidd et al. (2022, https://doi.org/10.3390/rs14132992) by applying the Precipitation Retrieval and Profiling Scheme to data observed by the NASA Time‐Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Millimeter‐wave Sounder (TMS) onboard the TROPICS Pathfinder satellite, a precursor to the TROPICS mission. We first compare the TMS precipitation retrieval performance over ocean with those from seven current operational passive microwave radiometers, including four conical scanning sensors [Special Sensor Microwave Imager/Sounders (SSMISs) onboard F16/F17/F18 satellites, and Advanced Microwave Scanning Radiometer 2 (AMSR2)] and three cross track scanning sensors [Microwave Humidity Sounders onboard NOAA19 and MetOpB satellites, and the Advanced Technology Microwave Sounder onboard the National Polar‐orbiting Partnership satellite], using precipitation estimates from Global Precipitation Measurement Mission Microwave Imager (GMI) as the reference. We show that TMS performs similarly to the current operational cross track scanning sensors, while worse than conical scanning sensors largely due to the lack of low frequency channels (e.g., 19 GHz). Second, we create an adjusted TMS precipitation retrievals by combining the original TMS precipitation and the precipitation propagated from AMSR2 and SSMISs to the time/location of the TMS overpass, using our previously developed microwave‐to‐microwave propagation technique. Results show that the adjusted TMS precipitation performs better than the original TMS estimates. For example, the correlation of the original TMS and GMI estimates is 0.35 but increases to 0.49 when the adjusted TMS result is compared to GMI. The propagation technique lays the foundation to improve TMS precipitation when incorporating them into level‐3 merged products.

Can We Estimate the Uncertainty Level of Satellite Long-Term Precipitation Records?

Abstract Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.