Global Precipitation Measurement Satellite Research Articles

Microphysics of Convective and Stratiform Precipitation during the Summer Monsoon Season over the Yangtze–Huaihe River Valley, China

Abstract Precipitation microphysics are critical for precipitation estimation and forecasting in numerical models. Using six years of observations from the Global Precipitation Measurement satellite, the spatial characteristics of precipitation microphysics are examined during the summer monsoon season over the Yangtze–Huaihe River valley. The results indicate that the heaviest convective rainfall is located mainly between the Huaihe and Yangtze Rivers, associated with a smaller mass-weighted mean diameter (Dm = ∼1.65 mm) and a larger mean generalized intercept parameter (Nw) (∼41 dBNw) at 2 km in altitude than those over the surrounding regions. Further, the convection in this region also has the lowest polarization-corrected temperature at 89 GHz (PCT89 < 254 K), indicating high concentrations of ice hydrometeors. For a given rainfall intensity, stratiform precipitation is characterized by a smaller mean Dm than convective precipitation. Below 4.5 km in altitude, the vertical slope of medium reflectivity factor varies with the rainfall intensity, which decreases slightly downward for light rain (<2.5 mm h−1), increases slightly for moderate rain (2.5–7.9 mm h−1), and increases more sharply for heavy rain (≥8 mm h−1) for both convective and stratiform precipitation. The increase in the amplitude of heavy rain for stratiform precipitation is much higher than that for convective precipitation, probably due to more efficient growth by warm rain processes. The PCT89 values have a greater potential to inform the near-surface microphysical parameters in convective precipitation compared with stratiform precipitation.

Passive Microwave Signatures and Retrieval of High-Latitude Snowfall Over Open Oceans and Sea Ice: Insights From Coincidences of GPM and CloudSat Satellites

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> database populated with the active–passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mmh <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> .

Unsupervised domain adaptation for Global Precipitation Measurement satellite constellation using Cycle Generative Adversarial Nets

AbstractArtificial intelligence has provided many breakthroughs in the field of computer vision. The fully convolutional networks U-Net in particular have provided very promising results in the problem of retrieving rain rates from space-borne observations, a challenge that has persisted over the past few decades. The rain intensity is estimated from the measurement of the brightness temperatures on different microwave channels. However, these channels are slightly different depending on the satellite. In the case where a retrieval model has been developed from a single satellite, it may be advantageous to use domain adaptation methods in order to make this model compatible with all the satellites of the constellation. In this proposed feasibility study, a Cycle Generative Adversarial Nets model is used for adapting one set of brightness temperature channels to another set. Results of a toy experiment show that this method is able to provide qualitatively good precipitation structure but still could be improved in terms of precision.

Three-Parameter Rain Drop Size Distributions From GPM Dual-Frequency Precipitation Radar Measurements: Techniques and Validation With Ground-Based Observations

Raindrop size distribution (DSD) parameters need to be estimated to determine rainfall from radar measurements of backscattered signals in the precipitating medium and to study microphysics of precipitation under varying atmospheric conditions. A technique is proposed to estimate the DSD parameters from Global Precipitation Measurement (GPM) dual-frequency products at Ku and Ka bands, which has been validated with ground-based disdrometer measurements obtained during the five-year period 2014-2018 over a tropical location, Kolkata (22°54’N, 88°35’E), India. The present method yields the three parameters of the DSD model in terms of a gamma function namely, intercept ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N₀</i> ), shape (μ) and slope (Λ). Based on a proposed optimization technique, the most appropriate μ-Λ relationship is determined during a GPM satellite pass to obtain the DSD parameters. The derived parameters have yielded mass-weighted mean diameter ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D_m</i> ) data that show much better agreement with ground-based DSD measurements than that provided by GPM DPR product. This method has also been used to obtain the height profiles of DSD that broadly correlate with ground-based micro rain radar (MRR) observations, showing how DSD varies at different altitudes. The present results demonstrate that the proposed technique is effective in obtaining three-parameter DSDs over a tropical location, and offers a useful method to study the microstructure of rain using space-borne radar.

Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency Using Spaceborne Radar and Microwave Imager of the Global Precipitation Measurement as Reference

Abstract In this study, the single-moment 6-class bulk cloud microphysics scheme used in the operational numerical weather prediction system at the Japan Meteorological Agency was improved using the observations of the Global Precipitation Measurement (GPM) core satellite as reference values. The original cloud microphysics scheme has the following biases: underestimation of cloud ice compared to the brightness temperature of the GPM Microwave Imager (GMI) and underestimation of the lower-troposphere rain compared to the reflectivity of GPM Dual-frequency Precipitation Radar (DPR). Furthermore, validation of the dual-frequency rate of reflectivity revealed that the dominant particles in the solid phase of simulation were graupel and deviated from DPR observation. The causes of these issues were investigated using a single-column kinematic model. The underestimation of cloud ice was caused by a high ice-to-snow conversion rate, and the underestimation of precipitation in the lower layers was caused by an excessive number of small-diameter rain particles. The parameterization of microphysics scheme is improved to eliminate the biases in the single-column model. In the forecast obtained using the improved scheme, the underestimation of cloud ice and rain is reduced. Consequently, the prediction errors of hydrometeors were reduced against the GPM satellite observations, and the atmospheric profiles and precipitation forecasts were improved.

Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

AbstractThe three-river headwaters region (TRHR) is the birthplace of the Yangtze River, the Yellow River and the Lantsang River in China. Based on the grid surface precipitation data released by China Meteorological Administration (CMA), this paper evaluated the accuracy and error components of four near-real-time satellite precipitation products (GSMaP-NRT, GSMaP-MVK, IMERG-Early and IMERG-Late) in the era of a GPM (Global Precipitation Measurement) in TRHR. The conclusions are as follows: (1) The precipitation in TRHR is concentrated in the east and south, and the precipitation in the west is very low. IMERG (Early and Late) has a good spatial distribution of precipitation, while GSMaP has an obvious spatial smoothing of precipitation distribution, and does not better highlight the local precipitation characteristics. (2) The inversion accuracy of the satellite products is the best in the source region of the Lantsang River, followed by the source region of the Yellow River. The satellite products all show the lower correlation coefficient and serious underestimation of precipitation in the west of the TRHR. In addition, the closer to the west of the TRHR, the lower hit rate and the higher false alarm rate of the satellite products, especially the NRT and MVK products. In the eastern margin of the Yellow River headwater region and the Lantsang River headwater Region, RMSE and overestimated precipitation were higher in NRT and MVK, and FAR was higher in spite of higher POD and CSI. (3) The errors of GSMaP in the source region of the Yellow River and the Lantsang River are mainly caused by misreporting precipitation and overestimating the precipitation level, while the errors of GSMaP in the west of the TRHR are mainly caused by missing measurements of precipitation events. The underestimated precipitation of IMERG mainly comes from the missed measurement of precipitation and the underestimate of precipitation level, and there is no large false precipitation. (4) In addition, we found that the satellite products in the lake distribution area of the TRHR have serious missed precipitation errors, indicating that the GPM satellite products have the poor detection ability of precipitation near plateau lakes. On the whole, the precipitation inversion accuracy of IMERG (Early and Late) products is higher, which can better detect the occurrence of precipitation events, but the estimation of precipitation level is still not accurate. The precision of precipitation of satellite products near inland lakes on the plateau is poor, so the algorithm improvement of new products needs to be further solved in the future.

Regionalization of Seasonal Precipitation over the Tibetan Plateau and Associated Large-Scale Atmospheric Systems

AbstractPrecipitation over the Tibetan Plateau (TP) has major societal impacts in South and East Asia, but its spatiotemporal variations are not well understood, mainly because of the sparsely distributed in situ observation sites. With the help of the Global Precipitation Measurement satellite product IMERG and the ERA5 dataset, distinct precipitation seasonality features over the TP were objectively classified using a self-organizing map algorithm fed with 10-day averaged precipitation from 2000 to 2019. The classification reveals three main precipitation regimes with distinct seasonality of precipitation: the winter peak, centered at the western plateau; the early summer peak, found on the eastern plateau; and the late summer peak, mainly located on the southwestern plateau. On a year-to-year basis, the winter peak regime is relatively robust, whereas the early summer and late summer peak regimes tend to shift mainly between the central and northern TP but are robust in the eastern and southwestern TP. A composite analysis shows that the winter peak regime experiences larger amounts of precipitation in winter and early spring when the westerly jet is anomalously strong to the north of the TP. Precipitation variations in the late summer peak regime are associated with intensity changes in the South Asian high and Indian summer monsoon. The precipitation in the early summer peak regime is correlated with the Indian summer monsoon together with anticyclonic circulation over the western North Pacific. The results provide a basic understanding of precipitation seasonality variations over the TP and associated large-scale conditions.

Assessment of Ground‐Based X‐Band Radar Reflectivity: Attenuation Correction and its Comparison With Space‐Borne Radars Over the Western Ghats, India

AbstractReflectivity measurements from X‐band ground‐based radar (GR) are assessed using observations from the Ku‐band space‐borne radars (SRs) onboard the Tropical Rainfall Measuring Mission and Global Precipitation Measurement satellites as a reference. The GR is at Mandhardev (18.04°N, 73.86°E) in the Western Ghats region, and the evaluation period is from June to September of 2014 and 2018. At X‐band, the rainfall‐induced attenuation leads to significant signal loss. The rain attenuation correction of GR is performed based on the relationship between specific attenuation and reflectivity. Using T‐matrix scattering simulations, the specific attenuation and reflectivity are derived from the disdrometer data. Further, the systematic differences between reflectivity measured by GR and SR are minimized by performing the frequency scaling using scattering simulation. The comparison between corrected reflectivities of GR and SR is achieved by matching the resolution volumes and viewing geometry of both radars. The evaluation between corrected reflectivities of GR and SR shows reasonable agreement between them, indicating that the attenuation correction performs reasonably for GR. In rain regions, the GR bias lies between −2.6 and −1.8 dB for stratiform cases, while for convective samples, the GR bias varies from −2.4 to −0.7 dB relative to SR observations. The GR shows smaller reflectivity compared to SR at all heights. This study aims to implement the attenuation correction to long‐term X‐band radar data available in the Western Ghats region to study the orographic precipitation and convective system during monsoon.

River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa

Satellite precipitation products have been largely improved in the recent years particularly with the launch of the global precipitation measurement (GPM) core satellite. Moreover, the development of techniques for exploiting the information provided by satellite soil moisture to complement/enhance precipitation products have improved the accuracy of accumulated rainfall estimates over land. Such satellite enhanced precipitation products, available with a short latency (< 1 day), represent an important and new source of information for river flow prediction and water resources management, particularly in developing countries in which ground observations are scarcely available and the access to such data is not always ensured. In this study, three recently developed rainfall products obtained from the integration of GPM rainfall and satellite soil moisture products have been used; namely GPM+SM2RAIN, PRISM-SMOS, and PRISM-SMAP. The prediction of observed daily river discharge at 10 basins located in Europe (4), West Africa (3) and South Africa (3) is carried out. For comparison, we have also considered three rainfall products based on: (1) GPM only, i.e., the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (GPM-ER), (2) rain gauges, i.e., the Global Precipitation Climatology Centre, and (3) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. Three different conceptual and lumped rainfall-runoff models are employed to obtain robust and reliable results over the 3-year data period 2015–2017. Results indicate that, particularly over scarcely gauged areas (West Africa), the integrated products outperform both ground- and reanalysis-based rainfall estimates. For all basins, the GPM+SM2RAIN product is performing the best among the short latency products with mean Kling–Gupta Efficiency (KGE) equal to 0.87, and significantly better than GPM-ER (mean KGE = 0.77). The integrated products are found to reproduce particularly well the high flows. These results highlight the strong need to disseminate such integrated satellite rainfall products for hydrological (and agricultural) applications in poorly gauged areas such as Africa and South America.

Precipitation Diurnal Cycle Assessment of Satellite-Based Estimates over Brazil

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.

Comprehensive evaluation of latest GPM era IMERG and GSMaP precipitation products over mainland China

Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the latest GPM era high-resolution satellite precipitation products (SPPs), namely Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP), have been released. A critical evaluation of these newly released SPPs is very important for both end users and data developers. This study aims to evaluate the performance of the IMERG and GSMaP products over mainland China from 1 January 2015 to 31 December 2017, using daily precipitation data from 778 meteorological stations. The SPPs are validated adopting three widely used statistical metrics (CC, RMSE and RB). The assessment also considers the precipitation detection capability (POD, FAR and CSI), probability density function (PDF) of precipitation intensities, and elevation factor. Results show that GSMaP_G performs better in daily, monthly and seasonal scales than other SPPs. In terms of CC and RMSE, all SPPs share similar spatial patterns, with high values in southeastern China and low values in northwestern China. In addition, all SPPs underestimate the precipitation amount in southern China, but overestimate that in northern China, especially in northwestern China. Obviously, the precipitation detection ability of all products is good in southern China, except the Sichuan Basin. All SPPs can better reproduce the PDF in terms of precipitation intensity. Compared with other SPPs, the performance of GSMaP_G is better at all elevations. However, significant elevation-dependent errors exist in the GSMaP_N. Overall, the gauge-corrected GSMaP_G and IMERG_F perform better than other SPPs in reducing various errors, especially the former. This study will be valuable for both algorithm developers and users, as well as for associated products, and it will also better support disaster risk reduction and hydrological applications, especially in areas with a sparse rain gauge network.

Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region

As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.

Merging Satellite and Gauge Rainfalls for Flood Forecasting of Two Catchments Under Different Climate Conditions

As satellite rainfall data has the advantages of wide spatial coverage and high spatial and temporal resolution, it is an important means to solve the problem of flood forecasting in ungauged basins (PUB). In this paper, two catchments under different conditions, Xin’an River Basin and Wuding River Basin, were selected as the representatives of humid and arid regions, respectively, and four kinds of satellite rainfall data of TRMM 3B42RT, TRMM 3B42V7, GPM IMERG Early, and GPM IMERG Late were selected to evaluate the monitoring accuracy of rainfall processes in the two catchments on hourly scale. Then, these satellite rainfall data were respectively integrated with the gauged data. HEC-HMS (The Hydrologic Engineering Center's-Hydrologic Modeling System) model was calibrated and validated to simulate flood events in the two catchments. Then, improvement effect of the rainfall merging on flood forecasting was evaluated. According to the research results, in most cases, the Nash–Sutcliffe efficiency coefficients of the simulated streamflow from initial TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) satellite rainfall data were negative at the two catchments. By merging gauge and TRMM rainfall, the Nash–Sutcliffe efficiency coefficient is mostly around 0.7, and the correlation coefficient is as high as 0.9 for streamflow simulation in the Xin'an River basin. For the streamflow simulated by merging gauge and GPM rainfall in Wuding River basin, the Nash–Sutcliffe efficiency coefficient is about 0.8, and the correlation coefficient is more than 0.9, which indicate good flood forecasting accuracy. Generally, higher performance statistics were obtained in the Xin'an River Basin than the Wuding River Basin. Compared with the streamflow simulated by the initial satellite rainfalls, significant improvement was obtained by the merged rainfall data, which indicates a good prospect for application of satellite rainfall in hydrological forecasting. In the future, it is necessary to further improve the monitoring accuracy of satellite rainfall products and to develop the method of merging multi-source rainfall data, so as to better applications in PUB and other hydrological researches.

Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data

Abstract. Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both of which are required inputs for event-based streamflow simulations. In this study, an existing dual state/rainfall correction system is extended and implemented in the 605 000 km2 Arkansas–Red River basin with a semi-distributed land surface model. The Soil Moisture Active Passive (SMAP) satellite surface SM retrievals are assimilated to simultaneously correct antecedent SM states in the model and rainfall estimates from the Global Precipitation Measurement (GPM) mission. While the GPM rainfall is corrected slightly to moderately, especially for larger events, the correction is smaller than that reported in past studies due primarily to the improved baseline quality of the new GPM satellite product. In addition, rainfall correction is poorer in regions with dense biomass due to lower SMAP quality. Nevertheless, SMAP-based dual state/rainfall correction is shown to generally improve streamflow estimates, as shown by comparisons with streamflow observations across eight Arkansas–Red River sub-basins. However, more substantial streamflow correction is limited by significant systematic errors present in model-based streamflow estimates that are uncorrectable via standard data assimilation techniques aimed solely at zero-mean random errors. These findings suggest that more substantial streamflow correction will likely require better quality SM observations as well as future research efforts aimed at reducing systematic errors in hydrologic systems.

A Recognition Method of Hydrometeor in Tropical Cyclones by Using the GPM Dual‐Frequency Precipitation Radar

In this paper, we propose a fuzzy logic algorithm to recognize the types of hydrometeors by using Ka and Ku reflectivity factors, temperature thresholds and an asymmetric t-form membership function. The identifiable types of hydrometeors include snow, graupel, mixed-phase particles, large raindrops, and small raindrops. The reflectivity detection data of Ka and Ku with attenuation correction is from dual-frequency precipitation radar onboard the global precipitation measurement satellite. The temperature data from the European Centre for Medium-range Weather Forecasts are used to identify the hydrometeors, and the identified hydrometeors are compared with the vertical profiles of phase from official level-2 DPR products. The results show that the identified hydrometeors are reasonable and reveal the evolution process of tropical cyclones, which can be further applied to the study of precipitation microphysical process and artificial precipitation.

Elucidating intra-seasonal characteristics of Indian summer monsoon. Part-I: Viewed from remote sensing observations, reanalysis and model datasets

In this study, we examine the transitions in the monsoon phases (onset, active, break and the withdrawal) during an entire monsoon season. This makes use of a host of observational tools that come from GPM (Global Precipitation Measurement) and TRMM (Tropical Rainfall Measuring Mission) satellites for precipitation estimates, the vertical structure of rain, hydrometeors and cloud types from TRMM and CloudSat datasets. During onset, the mean moisture convergence, especially over west and south-west coast of India is 2 × 10−4 kg m−1 s−1; however, it carries much higher value of >4 × 10−4 kg m−1 s−1 during the active phase over central eastern India. Much lesser moisture convergence (<1 × 10−4 kg m−1 s−1) is noted over Western Ghats area during the break phase. However, there are northeasterly moisture fluxes present over southern part of India during withdrawal phase. The tall cumulonimbus clouds that extend out to 16 km are illustrate during onset, the active phase is dominated by alto stratus and nimbostratus type clouds that are somewhat shallower. In general, we noted an absence of such clouds during the break and the withdrawal phases. Those structures were consistent in a number of derived fields such as the moisture convergence, moisture fluxes, the energy conversions between the rotational and the divergent kinetic energy and the corresponding phases of the intra-seasonal oscillations.

Regional Variability of Precipitation in Tropical Cyclones Over the Western North Pacific Revealed by the GPM Dual‐Frequency Precipitation Radar and Microwave Imager

AbstractA knowledge of precipitation microphysics of tropical cyclones (TCs) is crucial for forecasts of the TC track and intensity using numerical weather models. Based on five years of measurements of TCs over the western North Pacific from the dual‐frequency precipitation radar and the microwave imager on board the Global Precipitation Measurement satellite, the precipitation characteristics and microphysical processes in different regions of TCs and their associations with ice scattering signals are investigated. In the region close to the TC center, the storm top height (STH) and near‐surface rain rate are high, and the hydrometeors have a high concentration and a relatively low mass‐weighted mean diameter (Dm). In the outer TC region, the distributions of the reflectivity factor (Ze) and Dm become broader as the mean Dm increases. Ze and Dm values generally increase below the melting layer, indicating the predominant role of collision–coalescence processes. The occurrence probability of collision–coalescence is greater than 90% when STH is less than 5 km and the polarization‐corrected temperature at 89 GHz is greater than 250 K. When the STH exceeds 5 km, the collision–coalescence process is also predominant in the eyewall region; however, the influence of the breakup process increases in the rainband regions.

Constructing a Multifrequency Passive Microwave Hail Retrieval and Climatology in the GPM Domain

AbstractLarge hail is a primary contributor to damages and loss around the world, in both agriculture and infrastructure. The sensitivity of passive microwave radiometer measurements to scattering by hail led to the development of proxies for severe hail, most of which use brightness temperature thresholds from 37-GHz and higher-frequency microwave channels on board weather satellites in low-Earth orbit. Using 16+ years of data from the Tropical Rainfall Measuring Mission (TRMM; 36°S–36°N), we pair TRMM brightness temperature–derived precipitation features with surface hail reports in the United States to train a hail retrieval on passive microwave data from the 10-, 19-, 37-, and 85-GHz channels based on probability curves fit to the microwave data. We then apply this hail retrieval to features in the Global Precipitation Measurement (GPM) domain (from 69°S to 69°N) to develop a nearly global passive microwave–based climatology of hail. The extended domain of the GPM satellite into higher latitudes requires filtering out features that we believe are over icy and snowy surface regimes. We also normalize brightness temperature depression by tropopause height in an effort to account for differences in storm depth between the tropics and higher latitudes. Our results show the highest hail frequencies in the region of northern Argentina through Paraguay, Uruguay, and southern Brazil; the central United States; and a swath of Africa just south of the Sahel. Smaller hot spots include Pakistan, eastern India, and Bangladesh. A notable difference between these results and many prior satellite-based studies is that central Africa, while still active in our climatology, does not rival the aforementioned regions in retrieved hailstorm frequency.

Evaluating Simulated Microphysics during OLYMPEX Using GPM Satellite Observations

Abstract This study evaluates moist physics in the Weather Research and Forecasting (WRF) Model using observations collected during the Olympic Mountains Experiment (OLYMPEX) field campaign by the Global Precipitation Measurement (GPM) satellite, including data from the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) instruments. Even though WRF using Thompson et al. microphysics was able to realistically simulate water vapor concentrations approaching the barrier, there was underprediction of cloud water content and rain rates offshore and over western slopes of terrain. We showed that underprediction of rain rate occurred when cloud water was underpredicted, establishing a connection between cloud water and rain-rate deficits. Evaluations of vertical hydrometeor mixing ratio profiles indicated that WRF produced too little cloud water and rainwater content, particularly below 2.5 km, with excessive snow above this altitude. Simulated mixing ratio profiles were less influenced by coastal proximity or midlatitude storm sector than were GMI profiles. Evaluations of different synoptic storm sectors suggested that postfrontal storm sectors were simulated most realistically, while warm sectors had the largest errors. DPR observations confirm the underprediction of rain rates noted by GMI, with no dependence on whether rain occurs over land or water. Finally, WRF underpredicted radar reflectivity below 2 km and overpredicted above 2 km, consistent with GMI vertical mixing ratio profiles.

Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period

ABSTRACTRainfall is one of the key drivers of the global hydrological cycle and has large socio-economic impacts. Tropical rainfall accounts for two-thirds of the global rainfall and is primarily associated with the monsoon. Multi-satellite rainfall products provide rainfall with high temporal and spatial resolutions; however, they exhibit regional and seasonal biases. Evaluation of these products against ground-based observations can improve the accuracy of the estimated rainfall. With the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high-resolution multi-satellite precipitation products namely; Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are released. In the present study the spatial and temporal structures of rainfall in near real time and research versions of IMERG-V4 (near real-time (NRT) & Final (FNL)), GSMaP-V6 (NRT & moving vector with Kalman filter (MVK)), INSAT3D (Indian National Satellite System (INSAT) Multispectral Rainfall Algorithm (IMR) & Hydro-Estimator method (HEM)) and Indian Meteorological Department (IMD) – National Centre for Medium Range Weather Forecasting (NCMRWF) Merged product have been evaluated against gridded gauge-based IMD rainfall data on daily, monthly and seasonal scales. All the datasets show noticeable bias in producing rainfall over orographic regions (i.e. Western Ghats and foothills of Himalayas) and North-East India, though there exists significant difference among the satellite measurements. Different skill scores are computed for GSMaP, IMERG and INSAT3D data products to evaluate the performance of these satellite estimates. However in terms of biases IMD-NCMRWF Merged, GSMaP (NRT & MVK) and IMERG (NRT & FNL) underestimates rainfall (about 11%, 17%, 23%, 18% and 3%, respectively) and INSAT3D (IMR & HEM) overestimates (about 49% and 33%, respectively), for the India region as a whole. In a similar way, HEM product shows 15% better performance than IMR product in INSAT3D category. However, both NRT and MVK products of GSMaP show similar variations compared to observe rainfall. Overall IMD–NCMRWF merged and IMERG-FNL data products show better agreement with the gauge-based IMD data compared to GSMaP. The GPM-based products (IMERG and GSMaP) estimate rainfall much better than INSAT3D estimation.