Tropical Cyclone Rain Research Articles

Classification of tropical cyclone rain patterns using convolutional autoencoder

Heavy rainfall produced by tropical cyclones (TCs) frequently causes wide-spread damage. TCs have different patterns of rain depending on their development stage, geographical location, and surrounding environmental conditions. However, an objective system for classifying TC rain patterns has not yet been established. This study objectively classifies rain patterns of North Atlantic TCs using a Convolutional Autoencoder (CAE). The CAE is trained with 11,991 images of TC rain rates obtained from satellite precipitation estimates during 2000−2020. The CAE consists of an encoder which compresses the original TC rain image into low-dimensional features and a decoder which reconstructs an image from the compressed features. Then, TC rain images are classified by applying a k-means method to the compressed features from the CAE. We identified six TC rain patterns over the North Atlantic and confirmed that they exhibited unique characteristics in their spatial patterns (e.g., area, asymmetry, dispersion) and geographical locations. Furthermore, the characteristics of rain patterns in each cluster were closely related to storm intensity and surrounding environmental conditions of moisture supply, vertical wind shear, and land interaction. This classification of TC rain patterns and further investigations into their evolution and spatial variability can improve forecasts and help mitigate damage from these systems.

Spatiotemporal Characteristics of Tropical Cyclone Precipitation in Guangdong Province, China, from 1961 to 2020

Tropical cyclones (TCs) are often accompanied by heavy precipitation, which may lead to natural disasters and a serious threat to life and property. However, they also provide indispensable water resources. Studying the temporal and spatial characteristics of TC precipitation is of great importance for TC precipitation forecasting, TC disaster mitigation, and water resource utilization. Guangdong is one of the most frequently and severely TC-affected provinces in China. Due to the different methods used to identify TC precipitation, the conclusions offered by the existing studies are often inconsistent. Moreover, their analyses of the spatiotemporal characteristics of TC precipitation in Guangdong are not sufficiently thorough. In this study, we first selected the historical TCs that affected Guangdong from 1961 to 2020, using an objective separation method for TC wind and rain, based on the observation data from 86 national meteorological stations in Guangdong Province. From these observations covering the past 60 years, the temporal and spatial variations in TC precipitation in Guangdong for four different periods, namely the first rainy season (FRS), the second rainy season (SRS), the non-rainy season (NRS), and over the whole year (WY), were then explored using statistical analysis and multiple cluster methods. The results show that TC frequencies in the four periods all showed a decreasing trend. TC precipitation also showed a decreasing trend in the SRS and NRS, as well as for the WY, but showed a slightly increasing trend in the FRS. Both TC frequency and TC precipitation showed an apparent inter-annual fluctuation and a quasi-periodic pattern. The spatial distribution of TC precipitation in the four periods all showed a decreasing trend from the coastal to the inland stations, but the western coastal areas had higher TC precipitation values than the eastern coastal areas for the SRS, NRS, and WY periods. The spatial variations of TC precipitation in Guangdong in the four periods of the last six decades were quite similar, exhibiting three primary spatial modes and six patterns. Among them, the spatial distribution of TC precipitation being less than normal across the whole province is the most common pattern. The 86 stations can be classified into six groups when using the spatial clustering method and into four groups when using the time-series clustering method. Stations with higher TC precipitation and large inter-annual fluctuations are often distributed in the coastal areas, while stations with less precipitation and small inter-annual fluctuations are distributed in inland areas. However, the primary areas that are affected by TCs may vary in the different periods.

Assessing environmental conditions associated with spatially varying rainfall structure of North Atlantic tropical cyclones: An object‐based climatological analysis

AbstractThis study utilizes geographic information system‐based shape analyses with spatial regressions to examine the spatial variations in the relationships between North Atlantic TC rain field patterns and the environmental conditions. We measure the area, solidity, dispersion and closure of rain fields associated with North Atlantic tropical cyclones (TCs) during 1998–2014 from satellite‐based rain rate estimates. The potential contributing factors examined include storm intensity, translational speed, sea surface temperature, total precipitable water, upper‐tropospheric divergence, deep‐layer vertical wind shear and distance to land. Spatial metrics of rainfall, TC attributes and environmental conditions are aggregated onto a hexagon grid, and the average values are used in regression models. Spatial autoregressive regression and geographically weighted regression are applied to investigate the relationships between contributing factors and rainfall patterns. Storm intensity is positively correlated with area, solidity and closure and negatively correlated with dispersion. Higher moisture and larger upper‐level divergence contribute to a larger rainfall field with a more dispersed and less solid pattern, especially over the western Gulf and the western Caribbean. Wind shear and storm motion affect rain field patterns differently depending on the juxtaposition and relative magnitude of storm motion and wind shear vectors and thus exhibit more regional variations. This research further highlights the importance of incorporating a spatial component into the research on TC rainfall climatology. An improved understanding of basin‐wide and sub‐basin‐wide relationships between TC rain fields and contributing factors would benefit rainfall forecasting and hazard mitigation.

Does the Sierra Madre Mountain Range in Luzon Act as a Barrier to Typhoons?

Tropical cyclones (TCs) making landfall in Luzon weaken due to the surface friction of Luzon’s mountainous terrain and the reduced ocean heat, momentum, and moisture fluxes over the landmass. Landmass also influences TC rain distribution by orographic enhancement or blocking of a TC’s moisture-laden circulation. In this study, we investigated the influence of the effects of the Sierra Madre and the Cordillera Mountains Ranges (SMMR and CMR) on TC-associated wind and rainfall hazards to answer the question of whether the SMMR or the CMR mitigates TC hazards. We used the Weather Research and Forecasting (WRF) model with modified SMMR and CMR terrains to disentangle the effects of the orography with flat land. Results show that Luzon-passing TCs maintain their intensities at landfall regardless of the frictional effects of the mountain ranges but the CMR inhibits the re-intensification of westward-moving TCs emerging from landmass after traversing Luzon. The SMMR reduces wind exposure and basin-wide rainfall of the Cagayan Valley. Hence, the SMMR can be considered a barrier for that region. In addition, the weakening effect of the SMMR reduces the wind exposure of the island of Catanduanes and eastern Bicol the most. However, for the rest of Luzon, the SMMR enhances rainfall which will likely compensate for the slight decrease in wind exposure – especially considering that most TC-related damages are water/rainfall related. The CMR, overall, has a larger hazard-mitigating effect than the SMMR. In any case, we believe that shifting the discourse to these mountains’ biodiversity conservation and restoration – rather than their purported TC mitigating effects – will be more strategically constructive.

Analyzing Relationships between Tropical Cyclone Intensity and Rain Rate over the Ocean Using Numerical Simulations

Abstract In this study, the relationship between tropical cyclone (TC) intensity and rain rate over the ocean is investigated using a full-physics numerical model (WRF) and a physics-based TC rainfall model (TCR). TC intensity is found to be nearly linearly correlated with the average rain rate in the inner core [∼0.97 (mm h−1 m−2)/(m s−1)], while the correlation is weak at outer radii. This difference is induced because TC intensity is significantly correlated with both the vertical velocity and specific humidity in the inner core but is not significantly correlated with the vertical velocity in the outer radii. Further investigation shows that the intensity–rain-rate relationship at the outer radii is influenced by the TC evolution stage. The rain rate for the outer radii is positively correlated with TC intensity for nondecaying TCs, while this correlation is reduced for decaying TCs due to systematic downdrafts in the outer radii. In the context of climate change, the sensitivity of the TC rain rate to sea surface temperature (SST) is found to be +9% per 1 K increase of SST, roughly the product of the sensitivity of TC intensity to SST (+3%) and the Clausius-Clapeyron scaling (+7%). Coupled with synthetic storms, evolution of the TC rain rate over the twenty-first century under the SSP5-8.5 scenario is projected by the TCR (calibrated with the WRF simulations). The annual increase rates of averaged TC rain rate are 0.17% and 0.20% for the inner core and outer radii, respectively, larger than the annual increase rate of TC intensity (0.046%) but comparable to that of cube of intensity (0.18%).

Increase in tropical cyclone rain rate with translation speed

In general, tropical cyclone (TC) rainfall accumulation usually decreases with faster TC translation speed but increases with heavier rain rate. However, how the TC rain rate changes with translation speed is unclear. Here we show that, in all TC basins, the average TC rain rate significantly increases with translation speed. On average, the rain rate in a fast-moving TC is 24% higher than in a slow one. This difference increases with TC intensity, with category 3–5 TCs having a 42% increase while tropical depressions exhibit only a 9% increase. The increase in the average TC rain rate with translation speed is mainly caused by the TC net inflow in the lower troposphere, as well as vertical wind shear. These findings have important implications not only for a deeper understanding of rain rate changes in a translating TC but also for short-term forecasts of TC rainfall and disaster preparedness.

An Evaluation of Tropical Cyclone Rainfall Structures in the HighResMIP Simulations against Satellite Observations

Abstract This study evaluates tropical cyclone (TC) rainfall structures in the CMIP6 HighResMIP global climate model (GCM) simulations against satellite rainfall retrievals. We specifically focus on TCs within the deep tropics (25°S–25°N). Analysis of TC rain rate composites indicates that in comparison to the satellite observations at the same intensity, many HighResMIP simulations tend to overproduce rain rates around TCs, in terms of both maximum rain rate magnitude and area-averaged rain rates. In addition, as model horizontal resolution increases, the magnitude of the peak rain rate appears to increase. However, the area-averaged rain rates decrease with increasing horizontal resolution, partly due to the TC eyewall being located closer to the TC center, thus occupying a smaller area and contributing less to the area-averaged rain rates. The effect of ocean coupling is to lower the TC rain rates, bringing them closer to the satellite observations, due to reduced horizontal moisture flux convergence and surface latent heat flux beneath TCs. Examination of horizontal rain rate distributions indicates that vertical wind shear–induced rainfall asymmetries in HighResMIP-simulated TCs are qualitatively consistent with the observations. In addition, a positive relationship is observed between the area-averaged inner-core rainfall and TC intensification likelihoods across the HighResMIP simulations, as GCM simulations producing stronger TCs more frequently have the greater rainfall close to the center, in agreement with previous theoretical and GCM simulation results.

Assessment of Tropical Cyclone Rainfall from GSMaP and GPM Products and Their Application to Analog Forecasting in the Philippines

Tropical cyclone (TC) rainfall is both a resource and a hazard in the Philippines. Observation of its spatiotemporal distribution is necessary for water and disaster mitigation management. This study evaluated the performance of two high-resolution satellite precipitation datasets—the GSMaP and GPM-IMERG—in estimating accumulated TC rainfall in the Philippines from 2000 to 2021. TC rain is defined as rainfall within 5° of a TC center. Several estimation algorithms were included in the assessment. The uncalibrated near-real-time GSMaP_NRT and early version GPM_ER, the reanalysis GSMaP_RNL, and the gauge-calibrated GSMaP_G and GPM_G. The assessment shows the worst scores for the uncalibrated GSMaP_NRT and GSMaP_RNL, followed by GPM_ER with station correlation coefficient (CC) values of 0.63, 0.67, and 0.73, respectively, compared to GSMaP_G CC of 0.79 and GPM_G CC of 0.77. GSMaP_G and GPM_G also gave the least bias and error, with a consistently high (>0.6) probability of detection (POD) and Pierce skill score (PSS) up to rainfall of 300 mm. In addition to the evaluation, the GSMaP_G and GPM_G were used in the analog forecasting of TC rain. Analog forecasting is based on the principle that past weather conditions can occur again. In TC rain analog forecasting, past TCs with similar intensities and tracks are assumed to bring similar rainfall amounts and distribution as a current TC. Composite mean TC rainfall from historical satellite precipitation estimations was produced to create TC rain forecasts. Results show the analog TC rain forecasts generally captured the spatial distribution of TC rain and performed better than the uncalibrated GSMaP_NRT, with a mean station correlation of 0.62–0.67, POD greater than 0.7, and positive PSS indicating good skills. However, forecasts have a false alarm ratio greater than 0.8 for 150 mm rain and have difficulty producing extreme rainfall (>250 mm).

A Statistical-Based Model for Typhoon Rain Hazard Assessment

Extreme typhoon rainfall can lead to damaging floods near the coastal region in mainland China. In the present study, we calibrate the parameters for a parametric hurricane rain model by using the precipitation radar (PR) data from the Tropical Rainfall Measuring Mission (TRMM) (i.e., PR-TRMM) and the TRMM microwave imager (TMI) data (i.e., TMI-TRMM). To show the applicability of the model for the tropical cyclone (TC) rain hazard assessment, we combine the developed rainfall intensity model with historical and synthetic TC tracks to estimate the T-year return period value of the accumulated rainfall in 24 h, QA24-T. We map QA24-100 for part of the coastal region in mainland China, showing that the spatial variation of QA24-100 is relatively smooth. It was found that the estimated QA24-100 using the model developed, based on the snapshots from PR-TRMM, is about 60% of that obtained using the model developed based on the snapshots from TMI-TRMM. This reflects the differences in the rainfall intensities reported in TMI-TRMM and PR-TRMM. As part of verification, we compare the estimated return period value to that obtained by using the record from surface meteorological stations at a few locations. The comparison indicates that, on average, QA24-100 based on gauge data is about 1.4 and 2.3 times that obtained using the model developed based on the snapshots from PR-TRMM and TRM-TRMM, respectively. This suggests that, for TC rain hazard estimation, one may consider the empirical scaling factor of 1.4 and 2.4 for the rainfall intensity models developed based on snapshots from PR-TRMM and TMI-TRMM, respectively.

Opposite Changes in Tropical Cyclone Rain Rate During the Recent El Niño and La Niña Years

AbstractIn this paper, we investigate whether the relationship between the El Niño‐Southern Oscillation and tropical cyclone (TC) rain rate over the western North Pacific (WNP) changes between the global warming slowdown (2001–2012) and resumption (2013–2020) periods. Using high‐resolution satellite rainfall data, we show that the average TC rain rate increases by 9% but decreases by 14% during the recent El Niño and La Niña years (since 2013), respectively. During the El Niño years, the increase is related to higher TC track density at lower latitudes, where more water vapor and stronger convergence are present. On the other hand, the decrease in TC rain rate during the recent La Niña years is mainly related to an increase in track density at higher latitudes and a decrease in TC intensity. These changes in TC track density and intensity are both associated with the strengthening of the subtropical high since 2013.

Variability of tropical cyclone rainfall volume in the Philippines

AbstractThis study investigates the variability of tropical cyclone (TC)‐induced rainfall volume and its relation to the total rainfall in the Philippines. Of the total 1,843 TCs that formed in the western North Pacific (WNP) basin from 1951 to 2020, 983 (53%) and 1,253 (68%) TCs directly and indirectly, respectively, contributed 23.3% of the total rainfall on Philippine landmass. Total seasonal rainfall is negatively correlated with El Niño–Southern Oscillation (ENSO) (February–May [FMAM] r = −0.71 and October–January [ONDJ] r = −0.77) except for the boreal summer months of June–September (JJAS) where no correlation was observed. Similarly, TC rain also shows a negative but weaker correlation with ENSO (FMAM r = −0.23 and ONDJ r = −0.33) except for JJAS, despite JJAS season having the highest TC rain contribution. Nevertheless, all top 90th percentile TC rain years occurred during La Niña and ENSO‐neutral years, similar to the total rain. Rainfall volume from individual TCs depends mainly on its rain duration and the area of land affected. There are no significant trends found in annual and seasonal TC rainfall, but the frequency of extreme TCs with rainfall volume in the upper 95th percentile is dropping by 10%·decade−1. Annual TC rain volume is most correlated with TC landfall frequency (r = 0.81). Despite the influence of ENSO on TC activity and tracks in the NWP region, the results in this study suggest that the use of the ENSO Niño3.4 index alone may not be enough for seasonal TC rainfall forecasts.

Global increase in tropical cyclone rain rate

Theoretical models of the potential intensity of tropical cyclones (TCs) suggest that TC rainfall rates should increase in a warmer environment but limited observational evidence has been studied to test these hypotheses on a global scale. The present study explores the general trends of TC rainfall rates based on a 19-year (1998–2016) time series of continuous observational data collected by the Tropical Rainfall Measuring Mission and the Global Precipitation Measurement mission. Overall, observations exhibit an increasing trend in the average TC rainfall rate of about 1.3% per year, a fact that is contributed mainly by the combined effect of the reduction in the inner-core rainfall rate with the increase in rainfall rate on the rainband region. We found that the increasing trend is more pronounced in the Northwestern Pacific and North Atlantic than in other global basins, and it is relatively uniform for all TC intensities. Further analysis shows that these trends are associated with increases in sea surface temperature and total precipitable water in the TC environment.

Isotopic composition of rainfall in Baja California Sur, México

The study of the connection between precipitation and groundwater recharge/discharge using stable isotopes has been limited in México. One of the main limitations is the lack of local stable isotopes monitoring for rainfall. Indeed, only two stations of the Global Network of Isotopes in Precipitation operated in Mexico from 1962 to 1988. This study reports temporal characterization of stable isotopes in meteoric waters in the northwest of Mexico that has been lacking up to the present. Stable isotope analyses of the rain collected in the city of Loreto, state of Baja California Sur, from 2018 to 2021 were conducted using laser water isotope analyzers Picarro L2110-i and Los Gatos Research LWIA-45EP. A least squares regression of the isotope data was used to obtain the local meteoric water line: δ2H =8 δ18O + 9.4 (r2=0.98). The average value of winter rain is δ18O=-2.46 ‰ and δ2H=-10.2 ‰, monsoon rain δ18O=-4.93 ‰ y δ2H=-30.3 ‰, atmospheric river rain δ18O=-5.35 ‰ and δ2H=-33.3 ‰ and tropical cyclones rain δ18O= -10.7 ‰ y δ2H=-76.3. Isotope data indicated that tropical cyclones are the main source of recharge for the coastal aquifers of Baja California, Sur.

Analog forecasting of tropical cyclone rainfall in the Philippines

Tropical cyclone (TC) rainfall results in damages to properties and loss of lives. It is also a significant source of freshwater in the Philippines. This study describes a method in forecasting accumulated TC rainfall using analogous TCs from historical datasets. A TC rainfall database where precipitation within 5∘ of TC centers was created for all landfalling TCs from 1951 to 2015. To predict TC rainfall, the mean rainfall of all past TCs with similar tracks included in the database, referred to as analog TCs, is calculated. Landfalling TCs from 2016 to 2018 are used to optimize the selection of past analog TCs. Each past TC member was also adjusted according to a target TC's intensity and movement speed. The optimized analog method is then applied to landfalling TCs from 2019 to November 2020. Results show that the composite rainfall from past TCs within 1.8∘ of the forecast TC yields the best hit rate of intense rainfall. The analog TC rainfall forecast generally has a similar spatial distribution as the observed TC rain. However, this method tends to miss extreme rainfall values due to a “smoothing” effect caused by the variability of extreme rain locations of each TC member and constraints in the rainfall data used in the database. Nevertheless, forecast assessment results show that analog TC rainfall forecasting performed better than the WRF model in predicting intense and inland rainfall. In addition to it being computationally inexpensive, it can complement the inherent biases of dynamical models.

Hydrological Response of the Pampanga River Basin in the Philippines to Intense Tropical Cyclone Rainfall

AbstractThe Philippines is frequently affected by tropical cyclones (TCs), and understanding the flood response of the Pampanga River basin (PRB) from TC-induced rain is needed in effective disaster risk management. As large uncertainties remain in TC rain forecasting, we propose a simple checklist method for flood forecasting of the PRB that depends on the general TC track, season, and accumulated rainfall. To this end, flood events were selected based on the alert, alarm, and critical river height levels established by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). Results show that all flood events in the PRB were induced by TCs. All intense TCs that directly traversed the PRB resulted in critical-level floods. These TCs also had the shortest flood onset of 7–27 h from alert to critical level. Flooding from distant landfalling TCs, on the other hand, are dependent on season. TCs traversing north (south) of the PRB induced flooding only during the southwest (northeast) monsoon season. These TCs can raise water levels from alert to critical in 11–48 h. Remote precipitation from non-landfalling TCs can also induce critical-level flooding but with a longer onset time of 59 h. These results indicate that a simple checklist method can serve as a useful tool for flood forecasting in regions with limited data and forecasting resources.

Recent global decrease in the inner-core rain rate of tropical cyclones

Heavy rainfall is one of the major aspects of tropical cyclones (TC) and can cause substantial damages. Here, we show, based on satellite observational rainfall data and numerical model results, that between 1999 and 2018, the rain rate in the outer region of TCs has been increasing, but it has decreased significantly in the inner-core. Globally, the TC rain rate has increased by 8 ± 4% during this period, which is mainly contributed by an increase in rain rate in the TC outer region due to increasing water vapor availability in the atmosphere with rising surface temperature. On the other hand, the rain rate in the inner-core of TCs has decreased by 24 ± 3% during the same period. The decreasing trend in the inner-core rain rate likely results mainly from an increase in atmospheric stability.

Tropical Cyclone Wind Speed Estimation From Satellite Altimeter‐Derived Ocean Parameters

AbstractThis study investigates the satellite altimeter‐derived ocean parameters from Joint Altimetry Satellite Oceanography Network (Jason)‐2 and Jason‐3 response to the coincident wind speed at 10 m from sea surface (U10) from Meteorological Operational (MetOp)‐A and MetOp‐B satellite scatterometers inside the tropical cyclone environment. The high wind speed regime and rainy condition inside the tropical cyclone have reduced the U10 accuracy and thus, would benefit from more ocean related parameters to be considered. The altimeter Ku‐band backscatter and significant wave height are commonly used to estimate extreme U10 but still show significant error in rainy tropical cyclone. New U10 models incorporating several Jasons derived ocean parameters (including brightness temperatures and water content) were designed by the multiple linear regression and artificial neural network techniques. Parameter analysis indicated that the C‐band backscatter outperforms the Ku‐band counterpart in relation to U10 inside the tropical cyclone. The proposed models show U10 estimation up to 35 ms−1 with 1 ms−1 accuracy and major improvement inside the tropical cyclone rain and at the high wind speed regime. This too shows the ability of satellite altimeter in estimating U10 in extreme environment with great accuracy.

Observed Shear-Relative Rainfall Asymmetries Associated with Landfalling Tropical Cyclones

This study examines the shear-relative rainfall spatial distribution of tropical cyclones (TCs) during landfall based on the 19-year (1998–2016) TRMM satellite 3B42 rainfall estimate dataset and investigates the role of upper-tropospheric troughs on the rainfall intensity and distribution after TCs make a landfall over the six basins of Atlantic (ATL), eastern and central Pacific (EPA), northwestern Pacific (NWP), northern Indian Ocean (NIO), southern Indian Ocean (SIO), and South Pacific (SPA). The results show that the wavenumber 1 perturbation can contribute ∼ 50% of the total perturbation energy of total TC rainfall. Wavenumber 1 rainfall asymmetry presents the downshear-left maxima in the deep-layer vertical wind shear between 200 and 850 hPa for all the six basins prior to making a landfall. In general, wavenumber 1 rainfall tends to decrease less if there is an interaction between TCs and upper-level troughs located at the upstream of TCs over land. The maximum TC rain rate distributions tend to be located at the downshear-left (downshear) quadrant under the high (low)-potential vorticity conditions.

Common Ingredients and Orographic Rain Index (ORI) for Heavy Precipitation Associated with Tropical Cyclones Passing Over the Appalachian Mountains

Relative contributions of common ingredients to heavy orographic rainfall associated with the passage of Hurricanes Hugo (1989) and Isabel (2003) over the Appalachian Mountains are examined using a numerical weather prediction model. It is found that the key ingredients for producing local heavy orographic rainfall were: high precipitation efficiency, strong low-level flow, strong orographically forced upward motion associated with strong low-level flow over relatively gentle upslope, concave geometry providing local areas of convergence, high moist flow upstream, a relatively large convective system associated with both tropical cyclones (TCs), and relatively slower movement. In addition, neither conditional instability nor potential (convective) instability is found to play essential roles in producing strong upward motion leading to heavy orographic TC rain. A modified Orographic Rain Index (ORI) is proposed as a predictor for heavy orographic TC precipitation, which includes the upstream incoming horizontal wind speed normal to the local orography, the steepness of the mountain, the relative humidity, the TC moving speed, and the horizontal scale of the TC. It is found that the ORI estimated in regions of local maximum rainfall by using fine-resolution numerically simulated results correlate well with rainfall rates for both hurricanes, indicating that it may serve as a predictor for heavy orographic TC rainfall.

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.