GPM) Microwave Imager Research Articles

Evaluation of convective storms using spaceborne radars over the Indo‐Gangetic Plains and western coast of India

AbstractMonsoonal convective systems are examined using four years (2014–2017) of radar reflectivity data from the Precipitation Features (PFs) database of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual Precipitation Radar (DPR). The study classifies the cumulonimbus tower (PF‐CbT) at 12 km, and intense convective clouds at both 3 km (PF‐ICC3) and 8 km (PF‐ICC8) based on PFs' reflectivity threshold at a reference height over the Indo‐Gangetic Plains (IGP) and Indian Western coastal (WG) region, including the Arabian Sea and the Western Ghats. Results show that the regional variations are more enhanced for the PF‐ICC3 clouds with high occurrence over the IGP region. In the mixed‐phase regime, the median maximum reflectivity is greater for all cloud types over the IGP region. The occurrences of 20 and 40 dBZ echo the top height > 5 km is higher in the IGP region, indicating the deep and intense convection. The aerosol–cloud interaction is examined for warm and mixed‐phase clouds. The vertical structure of aerosols shows the suppression of warm rainfall over the IGP region. However, rainfall intensity increases in mixed‐phase clouds because of the dominancy of ice processes. The significant positive (negative) correlation is observed between the echo top height and aerosol concentrations over the IGP (WG) region. The value of the novel findings clearly states the region‐specific demand for a closer examination of the radar reflectivity–aerosol interaction on regime‐dependent clouds over the IGP region as well as contrasts against other regions for similar and contrasting cloud–aerosol‐radiation interactions.

Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events

AbstractThe National Aeronautics and Space Administration‐Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) mission consists of a multisatellite constellation that provides real‐time or near‐real‐time global observations of rain and snow. In this study, GPM Level 3 Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Level 2 GPM Microwave Imager Goddard Profiling rainfall products have been assimilated into the Weather Research and Forecasting model using the community Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments have been conducted and compared to demonstrate the impact of rain rate data assimilation on forecasts of heavy rainfall related to Hurricane Harvey (2017) and moderate to light rainfall observed during the GPM Integrated Precipitation and Hydrology Experiment field campaign. The results indicate that both GPM Microwave Imager Goddard Profiling and IMERG data could generate apparent increments in moisture, temperature, wind, and pressure fields for Hurricane Harvey, which led to significant improvement in the precipitation forecast. Frequent (every 3 hr) assimilation of IMERG data also positively impacted the short‐term precipitation forecast skill for the Integrated Precipitation and Hydrology Experiment moderate to light rain events. However, results also indicate that the impact of rain data assimilation was limited for a system that had a small horizontal dimension with low rain rates and within a relatively stable synoptic environment.

Retrieval of Sea Surface Temperature From HY-2A Scanning Microwave Radiometer

The scanning microwave radiometer (RM) onboard the Haiyang-2A (HY-2A) satellite has low-frequency channels with the capability of observing sea surface temperature (SST) from space. To improve the accuracy of the HY-2A RM SST data, the intercalibration of RM brightness temperature (BT) and SST retrieval were carried out. Based on the simulated BTs using the microwave radiative transfer model (RTM), the double-difference approach was applied to perform the intercalibration of RM BTs for the 10.7, 18.7, 23.8, and 37.0 GHz channels, with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) as the reference. The RM 6.6-GHz BTs were corrected with the RTM modeled BT on account of a lack of the similar channel in GMI. The comparison of RM original BTs with GMI and modeled BTs showed large biases and the strong dependence on latitude. We obtained latitude-dependent coefficients using robust linear regression for RM BT correction and applied recalibrated RM BTs for the SST retrieval. The validation of the RM retrieved SST showed the bias of −0.12 °C and the robust standard deviation (RSD) of 1.10 °C compared with buoy SST in the region between 70°S and 70°N. In the tropical and subtropical regions, the bias was −0.12 °C and the RSD was 0.93 °C. In addition, the relationships between the SST difference and the sea surface and atmospheric parameters were investigated. Both statistics of validation results and error analysis indicated significant improvement of RM SST accuracy.

Climatology and Detection of Overshooting Convection From 4 Years of GPM Precipitation Radar and Passive Microwave Observations

AbstractA 4‐year Global Precipitation Measurement (GPM) Precipitation Feature (PF) data set is used to quantify the frequency and global distribution of overshooting convection. In this study, overshooting convection is defined as PFs with maximum 20 dBZ echo top height (MAXHT20) greater than the height of the lapse rate tropopause, derived from ERA‐Interim reanalysis data. The geographical distribution of overshooting convection exhibits a strong preference for specific land regions, such as over the central United States, Argentina, Central Africa, and Colombia. Larger areas and greater occurrence of 20 dBZ radar reflectivity at the tropopause are found in northern middle to high latitudes than in the tropics. The occurrence of 20 dBZ radar reflectivity reaching above the level of 380 K potential temperature (Z380K) in middle and high latitudes is found to be comparable to that in the tropics. Furthermore, a methodology is developed to detect overshooting convection using the GPM Microwave Imager measured brightness temperature at 183.31 ± 3 and 183.31 ± 7 GHz, and Polarization Corrected Temperature at 89 GHz. The geographical distribution of overshooting convection can be closely reproduced using the combinations of these brightness temperatures with an average Heidke skill score of 0.4 and probability of 0.38. This shows the possibility of identifying overshooting convection from microwave observations at high‐frequency channels near the water vapor absorption line centered at 183.31 GHz from other satellite missions.

Performance of Radiative Transfer Models in the Microwave Region

AbstractWe compared two fast radiative transfer models, Community Radiative Transfer Model (CRTM) and Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV), with the LBL model Atmospheric Radiative Transfer Simulator (ARTS). We used the measurements from Advanced Technology Microwave Sounder (ATMS) and the Global Precipitation Measurement Microwave Imager (GMI) for evaluation of the radiative transfer models. The models in comparison with the observations and each other performed very well with a mean difference less than 0.5 K for the temperature sounding channels operating near the oxygen absorption band at 60 GHz. There was a difference of up to 1 K among the models as well as compared with the observations for humidity sounding channels operating around water vapor absorption line at 183 GHz. The mean difference between the simulations and observations was up to 6 K for surface sensitive channels. Water vapor and surface sensitive channels also showed to be more sensitive than the temperature sounding channels to the spectroscopy models used to calculate the absorption coefficients. There was a small difference, less than 0.1 K, between brightness temperatures calculated using traditional boxcar and actual Sensor or Spectral Response Functions, except for a difference of 0.25 K for ATMS Channel 6. Double difference technique showed about 1 K difference between water vapor channels from ATMS instruments onboard N20 and National Polar‐orbiting Partnership (NPP). However, comparison of a new version of ATMS/NPP observations recently generated using an enhanced calibration algorithm with ATMS/N20 observations showed that the differences between the two instruments are less than 0.5 K after improving the ATMS/NPP calibration.

An Active–Passive Microwave Land Surface Database From GPM

A microwave emissivity retrieval is applied to five years of global precipitation measurement (GPM) microwave imager (GMI) observations over land and sea ice. The emissivities are colocated with GPM’s dual-frequency precipitation radar (DPR) surface backscatter measurements in clear-sky conditions. The emissivity-backscatter database is used to characterize surfaces within the GPM orbit for precipitation retrieval algorithms and other applications. The full 10–166-GHz emissivity vector is retrieved using optimal estimation. Since GMI includes water vapor sounding channels, retrieval of the atmospheric and surface states are performed simultaneously. Using the MERRA2 reanalysis as the a priori atmospheric state and with proper characterization of its error, we are able to effectively screen for cloud- and precipitation-affected emissivities. Comparisons with colocated CloudSat data show that this GMI-based screen is able to detect precipitation that DPR alone does not; however, about 10% of precipitation occurrence from CloudSat is still undetected by GMI. The unsupervised Kohonen classification technique was then applied to multiyear monthly 0.25° gridded mean retrieved emissivities and backscatter distinctly for snow-free, snow-covered, and sea ice surfaces in order to classify surfaces based on both active and passive microwave characteristics. The classes correspond to vegetation coverage and type, inundation zones, soil composition, and terrain roughness. Snow and sea ice surfaces show clear seasonal cycles representing the increase in snow and ice spatial extent and reduction in the spring. Applications toward GPM precipitation retrieval algorithms and sensitivity to accumulated rain and snowfall are also explored.

Creating a Consistent Multi-Decadal Oceanic TRMM-GPM Brightness Temperature Record with Estimated Calibration Uncertainty

The National Aeronautics and Space Administration’s (NASA) Precipitation Measurement Missions (PMMs) include two earth satellite missions, namely, the Tropical Rainfall Measuring Mission (TRMM, 1997–2015) and the Global Precipitation Measurement (GPM, 2014-present). To generate a consistent multi-decadal brightness temperature (Tb) record that spans the TRMM and GPM eras, it is highly desirable to perform a comprehensive intercalibration of the TRMM Microwave Imager (TMI) and the GPM Microwave Imager (GMI) Tb measurements. Unfortunately, GMI and TMI share a limited common operational period of only 13 months. Fortunately, the WindSat polarimetric radiometer (2003-present) has been shown to be well calibrated and radiometrically stable relative to TMI for a period of over 5 years. Therefore, this paper describes the use of overlapping WindSat Tb measurements as the calibration bridge to achieve a seamless transfer joining the TMI and GMI Tb time series. Also, the development of the Tb measurement uncertainty estimation model is presented, which incorporates all relevant sources of uncertainty. Afterwards, this model was applied to three intercalibration processes: TMI to GMI, TMI to WindSat, and WindSat to GMI, and results are presented that quantify the corresponding Tb channel measurements biases and associated uncertainties associated with the merged TMI-GMI Tb record. This is an important accomplishment because this study can enable improved future Earth Science and global climate change investigations by making a long-term Tb record with estimated uncertainty available.

Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models

Abstract. Satellite microwave remote sensing is an important tool for determining the distribution of atmospheric ice globally. The upcoming Ice Cloud Imager (ICI) will provide unprecedented measurements at sub-millimetre frequencies, employing channels up to 664 GHz. However, the utilization of such measurements requires detailed data on how individual ice particles scatter and absorb radiation, i.e. single scattering data. Several single scattering databases are currently available, with the one by Eriksson et al. (2018) specifically tailored to ICI. This study attempts to validate and constrain the large set of particle models available in this database to a smaller and more manageable set. A combined active and passive model framework is developed and employed, which converts CloudSat observations to simulated brightness temperatures (TBs) measured by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and ICI. Simulations covering about 1 month in the tropical Pacific Ocean are performed, assuming different microphysical settings realized as combinations of the particle model and particle size distribution (PSD). Firstly, it is found that when the CloudSat inversions and the passive forward model are considered separately, the assumed particle model and PSD have a considerable impact on both radar-retrieved ice water content (IWC) and simulated TBs. Conversely, when the combined active and passive framework is employed instead, the uncertainty due to the assumed particle model is significantly reduced. Furthermore, simulated TBs for almost all the tested microphysical combinations, from a statistical point of view, agree well with GMI measurements (166, 186.31, and 190.31 GHz), indicating the robustness of the simulations. However, it is difficult to identify a particle model that outperforms any other. One aggregate particle model, composed of columns, yields marginally better agreement with GMI compared to the other particles, mainly for the most severe cases of deep convection. Of the tested PSDs, the one by McFarquhar and Heymsfield (1997) is found to give the best overall agreement with GMI and also yields radar dBZ–IWC relationships closely matching measurements by Protat et al. (2016). Only one particle, modelled as an air–ice mixture spheroid, performs poorly overall. On the other hand, simulations at the higher ICI frequencies (328.65, 334.65, and 668.2 GHz) show significantly higher sensitivity to the assumed particle model. This study thus points to the potential use of combined ICI and 94 GHz radar measurements to constrain ice hydrometeor properties in radiative transfer (RT) using the method demonstrated in this paper.

Evaluation of the Initial Sea Surface Temperature From the HY-2B Scanning Microwave Radiometer

Haiyang-2B (HY-2B) is the second marine dynamic environment satellite of China. Sea surface temperature (SST) products from the scanning microwave radiometer (SMR) onboard HY-2B satellite are evaluated against in situ measurements. Approximately, ten months of data are used for the initial evaluation, from January 15, 2019 to November 15, 2019. The temporal and spatial windows for collocation are 30 min and 25 km, respectively, which produce 450 416 matchup pairs between HY-2B/SMR and in situ SSTs. The statistical comparison of the entire data set shows that the mean bias is −0.13 °C (SMR minus buoy), and the corresponding root-mean-square error (RMSE) is 1.06 °C. Time series of collocations for the SST difference shows that a good agreement is found between HY-2B/SMR and in situ SSTs after June 15, revealing a mean bias and an RMSE of only 0.09 °C and 0.72 °C, respectively. A three-way error analysis is conducted between the SMR, Global Precipitation Measurement Microwave Imager (GMI), and in situ SSTs. Individual standard deviations are found to be 0.41 °C for the GMI SST, 0.15 °C for the in situ SST, and 1.03 °C for the SMR SST. The results indicate that the HY2B/SMR SST products need to be improved during the period from January 15, 2019 to June 15, 2019.

Synergy between INSAT-3D infra-red and GPM microwave radiometer for precipitation studies

The Indian National Satellite System (INSAT) based Multispectral Rainfall Algorithm (IMSRA) operationally provides precipitation estimates using measurements provided by the Kalpana satellite from 2008 onwards and the INSAT-3D from 2014 onwards over Indian land and oceanic regions for dissemination to the users. Here, an attempt is made to estimate rainfall over the Indian monsoon region by the synergistic use of geostationary INSAT-3D IMSRA derived rainfall estimates and rain data from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) using a suitable objective analysis method. The objective criteria of a successive correction method benefit from high spatial and temporal resolutions of the geostationary satellite and infrequent but more accurate microwave-based rainfall estimates, particularly during heavy rain conditions. The method is applied to various case studies for the southwest monsoon season of 2015 at both instantaneous and daily scale. A comparison is made with ground data and also with independent global concurrent rainfall products. The merged rainfall estimates shows noticeable improvement over the Infrared (IR)-based rainfall estimates over various parts of the Indian land and oceanic regions on a daily scale. Overall, results reveal that the GMI type radiometer in low inclination orbit like the Megha-Tropiques and Tropical Rainfall Measurement Mission (TRMM) could be more useful for synergistic use with INSAT-3D. It has the potential to predict more accurately near real-time tropical rainfall, particularly over the Indian monsoon region, which makes it a valuable source of information for flood forecasting, agriculture and water resource management, numerical weather prediction, and numerous other applications in the hydro-meteorological sciences.

Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China

Abstract The performance of the Version 06B monthly Integrated Multi-satellite Retrievals for GPM (IMERG) as well as three satellite precipitation products, including the Version 7 monthly TRMM Multi-satellite Precipitation Analysis (TMPA 3B43), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is evaluated on multiple temporal scales over a typical agriculture area in China (Huanghuaihai Plain) from 2015 to 2017. The results show that four satellite-based precipitation products have good agreement with rain gauge measurements at annual scale (mean CC: 0.92, mean RMSE: 205.52 mm, mean RB: 6.57%) and on a seasonal scale (mean CC: 0.88, mean RMSE: 67.04 mm, mean RB: 9.57%) but perform relatively poorly on a monthly scale with mean CC of 0.83. All satellite precipitation products can capture the spatial distribution pattern of rain gauge that performs adequately in spring and winter both with a mean CC of 0.95. However, except for IMERG V06B (CC: 0.90, RMSE: 86.99 mm, RB: 6.62%), other satellite precipitation datasets showed poor performance in summer with a mean CC of 0.76 and a mean RMSE of 133.88 mm. Overall, the IMERG V06B product has the best performance on the monthly, seasonal and annual scales over the Huanghuaihai Plain. The reason is that IMERG V06B, with higher spatiotemporal resolution and improvement of retrieval algorithm, can obtain more detailed precipitation information. In addition, GPM carries the first spaceborne Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a multi-channel GPM Microwave Imager (GMI), which makes it easier to detecting light rainfall and snowfall. The results also show that the performance of IMERG V06B at each individual gauge station is better than those of other products with lower RMSEs and higher CCs. Moreover, the relatively high probability of detection (POD) and low false alarm ratio (FAR) of the IMERG V06B product over most ranges of precipitation thresholds also indicated the good light rainfall and snowfall detection capability over the Huanghuaihai Plain. However, the performance of satellite-based precipitation products is affected by local climate and topography, which lead to poor performances in summer and early autumn due to heavy rainfall over the Huanghuaihai Plain. Therefore, the estimation accuracy of satellite-based precipitation products in different regions needs to be improved in the future. This study will contribute to the improvement of satellite retrieval algorithm and provide guidance in agricultural production and natural hazard prevention in the Huanghuaihai Plain.

Uncertainties of GPM Microwave Imager Precipitation Estimates Related to Precipitation System Size and Intensity

AbstractThe uncertainties in the version 5 Global Precipitation Measurement (GPM) Microwave Imager (GMI) precipitation retrievals are evaluated via comparison with the radar–radiometer (so-called “Combined”) retrievals between 40°S and 40°N. Results show the precipitation estimates are close (~7% GMI overestimation) globally. However, some specific regions, such as central Africa, the Amazon, the Himalayan region, and the tropical eastern Pacific, show a large overestimation (up to 50%) in GMI retrievals when compared to Combined retrievals. The uncertainties are further evaluated based on precipitation system properties, such as size and intensity of the system. GMI tends to underestimate precipitation volume when the system is relatively warm (>250 K) and small (<200 km2) due to the lack of ice scattering signatures. However, for large systems (>2000 km2), GMI-derived precipitation is typically higher than Combined over all surfaces. Based on the system properties, a simple bias correction methodology is proposed to implement in the Goddard Profiling Algorithm (GPROF) to reduce GMI biases. GMI precipitation volume is adjusted in each precipitation system based on the size and minimum 89 GHz polarization-corrected temperature (PCT) over land and ocean separately. The overall GMI bias is reduced to 3%, with significant improvement over land. The GMI biases (up to 50%) over the previously mentioned regions are significantly or partially removed, becoming less than 20%. This method also shows effectiveness in removing zonal and seasonal biases from GMI estimates. These results suggest the importance of utilizing the information of whole precipitation systems instead of individual pixels in the precipitation retrieval.

Detection of Radio Frequency Interference in Microwave Radiometers Operating in Shared Spectrum

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at −5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate.

Identification of Sun Glint Contamination in GMI Measurements Over the Global Ocean

This paper utilizes the model regression difference method to identify sun glint contamination on Global Precipitation Measurement Microwave Imager (GMI) data over the ocean based on observations from 2015 to 2016. The spatial distribution characteristics and the critical angles of the sun glint flags are analyzed in depth. It is found that the GMI measurements with horizontal and vertical polarizations at 10.65 GHz over the ocean are sometimes contaminated by the solar radiation reflected by the sea surface. The sun glint contamination has also been detected over high reflected land surface. The intensity and locations of the contamination are related to the sun glint angle. Only those GMI field of views with smaller sun glint angles are easily contaminated. The closer the sun glint angle is to 0°, the stronger the magnitude of the contamination. The GMI observations at other channels are not contaminated mainly because sun glint is most pronounced at 10 GHz. There are too strong constraints and tossing out of too many useful data in current GMI sun glint algorithms. The suggested critical angles of the sun glint flags for 10.65GHz is 20° to reduce false flagging. By applying the model regression difference method, the error in brightness temperature caused by sun glint can be corrected. The Tropical Rainfall Measuring Mission Microwave Imager (TMI) observations at 10.65 GHz are also contaminated by the reflected solar radiation from the ocean, and the intensity and locations of the contamination are similar to those of the GMI.

CoSMIR Performance During the GPM OLYMPEX Campaign

The airborne Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) has participated in the Global Precipitation Measurement (GPM) Olympic Mountains Experiment (OLYMPEX) from November to December, 2015, with great success. With similar channels as that of the GPM Microwave Imager (GMI) at 89–183 GHz, CoSMIR served as a proxy for GMI by flying onboard the DC-8 Aircraft for a total of 17 science flights, collecting over 72 h of observations. The high-quality, calibrated brightness temperature data set is the result of several improvements made to CoSMIR prior to OLYMPEX to make the instrument more reliable. This paper describes these improvements and gives a detailed summary of the CoSMIR measurements obtained from OLYMPEX. CoSMIR experienced minor performance issues during the campaign, most of them were not excessive and only resulted in a loss of approximately 4 h of data for the entire campaign. Performance issues are discussed and shown how they were mitigated to achieve a quality data set. Comparisons of CoSMIR and GMI observations are presented to show that the CoSMIR measurements agree well with GMI. The CoSMIR data set is publicly available as a part of the OLYMPEX data suite and can reliably be used in the GPM algorithm development and related studies.

Sensitivity of Simulated GMI Brightness Temperatures to Variations in Particle Size Distributions in a Severe Hailstorm

AbstractGlobal Precipitation Measurement (GPM) Microwave Imager (GMI) brightness temperatures (BTs) were simulated over a case of severe convection in Texas using ground-based S-band radar and the Atmospheric Radiative Transfer Simulator. The median particle diameter Do of a normalized gamma distribution was varied for different hydrometeor types under the constraint of fixed radar reflectivity to better understand how simulated GMI BTs respond to changing particle size distribution parameters. In addition, simulations were conducted to assess how low BTs may be expected to reach from realistic (although extreme) particle sizes or concentrations. Results indicate that increasing Do for cloud ice, graupel, and/or hail leads to warmer BTs (i.e., weaker scattering signature) at various frequencies. Channels at 166.0 and 183.31 ± 7 GHz are most sensitive to changing Do of cloud ice, channels at ≥89.0 GHz are most sensitive to changing Do of graupel, and at 18.7 and 36.5 GHz they show the greatest sensitivity to hail Do. Simulations contrasting BTs above high concentrations of small (0.5-cm diameter) and low concentrations of large (20-cm diameter) hailstones distributed evenly across a satellite pixel showed much greater scattering using the higher concentration of smaller hailstones with BTs as low as ~110, ~33, ~22, ~46, ~100, and ~106 K at 10.65, 18.7, 36.5, 89.0, 166.0, and 183.31 ± 7 GHz, respectively. These results suggest that number concentration is more important for scattering than particle size given a constant S-band radar reflectivity.

Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)

AbstractThis study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

The Global Flood Detection Systems (GFDS) currently operated at the European Commission’s Joint Research Centre (JRC) is a satellite-based observation system that provides daily stream flow measurements of global rivers. The system was initially established using NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) Ka-band passive microwave satellite data. Since its initiation in 2006, the methodology and the GFDS database have been further adapted for data acquired by the Tropical Rainfall Measuring Mission (TRMM) GOES Precipitation Index (GPI), the AMSR2 sensor onboard the Global Change Observation Mission – Water satellite (GCOM-W1), and the Global Precipitation Measurement (GPM) GPM Microwave Imager (GMI) sensor. This paper extends the same flow monitoring methodology to low frequency (L-band) passive microwave observations obtained by the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) sensor that was launched in 2009. A primary focus is tropical climate regions with dense rainforest vegetation (the Amazon, the Orinoco, and the Congo basins) where high-frequency microwave observations from GFDS reveal a significant influence of vegetation cover and atmospheric humidity. In contrast, SMOS passive microwave signatures at the much lower L-band frequency exhibit deeper penetration through the dense vegetation and minimal atmospheric effects, enabling more robust river stage retrievals in these regions. The SMOS satellite river gauging observations are for 2010–2018 and are compared to single-sensor GFDS data over several river sites. To reduce noise, different filtering techniques were tested to select the one most suitable for analysis of the L-band time series information. In-situ water level (stage) measurements from the French Observation Service SO Hybam database were used for validation to further evaluate the performance of the SMOS data series. In addition to GFDS data, water stage information from Jason-2 and Jason-3 altimetry was compared to the microwave results. Correlation of SMOS gauging time series with in-situ stage data revealed a good agreement (r = 0.8–0.94) during the analyzed period of 2010–2018. Moderate correlation was found with both high frequency GFDS data series and altimetry data series. With lower vegetation attenuation, SMOS signatures exhibited a robust linear relationship with river stage without seasonal bias from the complex hysteresis effects that appeared in the Ka-band observations, apparently due to different attenuation impacts through dense forests at different seasonal vegetation stages.

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.

Assessment of GPM high‐frequency microwave measurements with radiative transfer simulation under snowfall conditions

The Global Precipitation Mission (GPM) Microwave Imager (GMI) has four channels at 166 and 183 GHz that provide critical information on snow precipitation. Since the applications of these high‐frequency microwave channels to snowfall prediction and retrieval are still in a very early stage, it is important to evaluate the biases between observed and simulated brightness temperatures for those channels under snowfall conditions. A radiative transfer model that supports the computation of single‐scattering properties for non‐spherical snow particles is employed to simulate GMI brightness temperatures. Snow water content profiles are first derived from the CloudSat Cloud Profiling Radar (CPR) measurements and then used as input to the radiative transfer model. Results show that the biases of observed minus simulated brightness temperatures for GMI channels averaged over all selected pixels are generally less than 1 K under snowfall conditions, except for the 166 GHz horizontal polarization (166H) channel that has a bias of about 3 K. Larger biases are observed under snowfall conditions when the scene brightness temperatures are low. Further investigation indicates that the GMI remaining biases are often associated with shallow snow cells or deep convective clouds. We hypothesize that in shallow snow cells the errors in retrieved cloud liquid water likely contribute to the large bias for the GMI 166H channel, while in deep convective clouds the attenuation in CPR radar reflectivities and possible sampling bias likely cause the biases for GMI channels under low scene brightness temperatures. This study lays out an applicable configuration in simulating microwave brightness temperatures at high frequency under snowfall conditions, and the explanation provided for the GMI remaining biases at 166 and 183 GHz are useful for future applications of these channels.