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This study focuses on the considerable spatial variability of precipitation along the western coast of a continent at mid-high latitude and investigates the precipitation climatology and mechanism along the south coast of Alaska, using datasets of spaceborne radars onboard two satellites, namely, the Dual-frequency Precipitation Radar (DPR) KuPR onboard the Global Precipitation Measurement (GPM) core satellite and the Cloud Profiling Radar (CPR) onboard CloudSat. At higher latitudes, differentiating the phase of precipitation particles falling on the ground is crucial in evaluating precipitation. Classification of satellite precipitation products according to the distance from the coastline shows that precipitation characteristics differ greatly on opposite sides of the coastline. Above coastal waters, relatively heavy precipitation with CPR reflectivity larger than 7 dBZ from orographically enhanced nimbostratus clouds, which can be detected by KuPR, is frequently captured. Meanwhile, along coastal mountains, light-to-moderate snowfall events with CPR reflectivity lower than 11 dBZ, which are well detected by the CPR but rarely detected by KuPR, frequently occur, and they are mainly brought by nimbostratus clouds advected from the coast and orographically enhanced shallow cumuliform clouds. There is no clear diurnal variation of precipitation except in summer, and the amplitude of the variation during summer is still low compared with total precipitation especially over the ocean, suggesting that the transport of synoptic-scale water vapor brings much precipitation throughout the year. Case studies and seasonal analysis indicate that frontal systems and moisture flows associated with extratropical cyclones that arrive from the Gulf of Alaska are blocked by terrain and stagnate along the coast to yield long-lasting precipitation along the coastline. The results of this study illustrate the importance of using complementary information provided by these radars to evaluate the precipitation climatology in a region in which both rainfall and snowfall occur., 本研究は、空間変動の大きい中高緯度大陸西岸の降水に焦点を当て、全球降水観測計画（GPM）主衛星搭載二周波降水レーダ（DPR）Ku帯降水レーダ（KuPR）およびCloudSat衛星搭載雲レーダ（CPR）を用いてアラスカ南岸の気候学的な降水分布や降水メカニズムについて調査した。高緯度では地表へ落下する降水粒子の相を判別することが降水を評価するうえで不可欠である。海岸線からの距離によって衛星降水プロダクトを分類することで、海岸線を挟んだ海側と陸側で降水特性が大きく異なっていることを示した。沿岸の海上では、地形効果で強化された乱層雲からのCPR反射強度７dBZ以上の比較的強い降水が頻繁にとらえられており、KuPRでもとらえられている。一方、海岸山脈上では、CPR反射強度11dBZ以下の弱～中程度の降雪が頻繁に発生していることが、CPRでとらえられているがKuPRではほとんどとらえられていない。この雪は主に海岸域より移流してきた乱層雲や地形効果を受けて強まった浅い対流雲によってもたらされている。夏季を除いて顕著な降水の日周期変動はなく、さらに夏季の日周期変動の振幅も総降水量と比べると特に海上で小さく、総観規模の水蒸気輸送が年間を通して多くの降水をもたらしていることを示唆している。事例解析と季節解析により、アラスカ湾から到来する温帯低気圧に伴う前線システム及び水蒸気の流れが、海岸沿いで地形によりブロックされて停滞し、沿岸に長く持続した降水をもたらしていることが示された。本研究の結果は、降雨・降雪の両方が発生する地域の降水気候値を評価するには、これら２つのレーダの相補的な情報を用いることが重要であることを示している。

The Global Satellite Mapping for Precipitation (GSMaP) produces high-resolution and high-frequent global rainfall map based on multi-satellite passive microwave radiometer observations with information from the Geostationary InfraRed (IR) instruments (Kubota et al. 2020). Output product of GSMaP algorithm is 0.1-degree grid for horizontal resolution and 1-hour for temporal resolution. The GSMaP near-real-time version product (GSMaP_NRT) has been in operation at JAXA since November 2007 in near-real-time basis, and browse images and binary data available at JAXA GSMaP web site (http://sharaku.eorc.jaxa.jp/GSMaP/).A new version of the GSMaP product was released in December 2021. We plan the reprocessing of the GSMaP standard version in a period during the past 24 years since Jan. 1998. The GSMaP algorithms consist of passive microwave (PMW) algorithms, a normalization module for PMW retrievals, a PMW-IR Combined algorithm, and a Gauge-adjustment algorithm. Features in the new version are summarized as follows. In the PMW algorithm, retrievals extended to the pole-to-pole. Databases used in the algorithm were updated. A method using frozen precipitation depths was newly installed (Aonashi et al. 2021). Heavy orographic rainfall retrievals were improved upon a basic idea of Shige and Kummerow (2016). The normalization module for PMW retrievals (Yamamoto and Kubota 2020) were newly implemented to make more homogeneous PMW retrievals, in particular, for microwave sounders. A basic idea of the PMW-IR combined algorithm is using morphing and Kalman filter (Ushio et al. 2009). In addition, a histogram matching method by Hirose et al. (2022) was implemented in the new version to reduce the IR retrievals with reference to the PMW retrievals. In the gauge-adjustment algorithm, a precipitation estimate is adjusted using the NOAA CPC Global Unified Gauge-Based Analysis of Daily Precipitation (Mega et al. 2019). Artificial patterns appeared in past versions were mitigated in the new version. Preliminary validation results using the gauge-adjustment ground radar data over the Japan land areas confirmed better results in the new version of the satellite only products.Furthermore, the GSMaP real-time version (GSMaP_NOW) with the new algorithm was also released in December 2021.The GSMaP algorithm for the new version was also applied to the GSMaP_NOW system after 6th December 2021. Accuracy improvements were confirmed also in the GSMaP_NOW products by validations with the gauge-adjustment ground radar data over Japan.

We have improved a rainfall retrieval algorithm over coast for the Advanced Microwave Sounding Unit (AMSU) in order to upgrade to the Global Satellite Mapping of Precipitation (GSMaP) product version 8. As for the algorithm of product version 7, the zonal-mean monthly precipitation tends to overestimate low latitude coastal areas and to be a missing value the polar side of the winter hemisphere from 40 degrees latitude due to the low temperature screening. In order to improve these problems, we separated the coast into the seaward and landward coasts, and reconsidered Rain/No-Rain classification method, screening conditions and estimation method of each algorithm.

Rainfall over the coastal regions of western India [Western Ghats (WG)] and Myanmar [Arakan Yoma (AY)], two regions experiencing the heaviest rainfall during the Asian summer monsoon, is examined using a Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) dataset spanning 16 years. Rainfall maxima are identified on the upslope of the WG and the coastline of AY, in contrast to the offshore locations observed in previous studies. Continuous rain with slight nocturnal and afternoon–evening maxima occurs over the upslope of the WG, while an afternoon peak over the upslope and a morning peak just off the coast are found in AY, resulting in different locations of the rainfall maxima for the WG (upslope) and AY (coastline). Large rainfall amounts with small diurnal amplitudes are observed over the WG and AY under strong environmental flow perpendicular to the coastal mountains, and vice versa. Composite analysis of the boreal summer intraseasonal oscillation (BSISO) shows that the rain anomaly over the WG slopes lags behind the northward-propagating major rainband. The cyclonic systems associated with the BSISO introduces a southwest wind anomaly behind the major rainband, enhancing the orographic rainfall over the WG, and resulting in the phase lag. This lag is not observed in the AY region where more closed cyclonic circulations occur. Diurnal variations in rainfall over the WG regions are smallest during the strongest BSISO rainfall anomaly phase.

An orographic/nonorographic rainfall classification scheme has been introduced for the operational algorithm of the Global Satellite Mapping of Precipitation (GSMaP) for passive microwave radiometers. However, problems of overestimations and false alarms of heavy orographic rainfall remain unresolved. This is because the current scheme selected lower constant thresholds of orographic rainfall conditions for global application and used values of orographically forced upward motion w derived from near-surface atmospheric data. This study improves the conceptual model of the warm-rain process for considering the strength of the upstream flow of the low-level troposphere. Under a weak upstream current, rain reaches the foothills of the windward mountain slope because of sufficient time for condensation and precipitation enhancement by the topography. Conversely, under a strong upstream current, precipitation enhancement occurs nearer to the mountain peak. This is because the upstream current flows so quickly that there is insufficient time for enhancement of precipitation over the foothills of the windward mountain slope. After implementing a variable threshold for w that depends on the mean horizontal low-level wind, the area of orographic enhancement of rain was detected reasonably well in cases of both strong and weak winds. To improve the accuracy of estimates of orographic rainfall, an adjustment to the rain estimation was introduced using a lower-frequency channel. The biases of the rainfall estimate for the adjusted scheme from the Tropical Rainfall Measuring Mission Precipitation Radar were improved for the cases considered here as well as for the Asian region of heavy orographic rainfall over land.

In this study, the spatial variability in precipitation at a 0.1° scale is investigated using long-term data from the Tropical Rainfall Measuring Mission Precipitation Radar. Marked regional heterogeneities emerged for orographic rainfall on characteristic scales of tens of kilometers, high concentrations of small-scale systems (100 km) result in more rainfall over the adjacent ocean. Finescale hourly data represented the abrupt asymmetric fluctuation in rainfall across the coastline in the tropics and subtropics (30°S–30°N). Significant diurnal modulations in the rainfall due to large-scale systems are found over tropical offshore regions of vast landmasses but not over small islands or in the midlatitudes between 30° and 36°. Rainfall enhancement over small tropical islands is generated by abundant afternoon rainfall, which results from medium-scale storms that are regulated by the island size and inactivity of rainfall over coastal waters.

Observations of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) over 16 yr yielded hundreds of large precipitation systems (≥100 km) for each 0.1° grid over major rainy regions. More than 90% of the rainfall was attributed to large systems over certain midlatitude regions such as La Plata basin and the East China Sea. The accumulation of high-impact snapshots reduced the significant spatial fluctuation of the rain fraction arising from large systems and allowed the obtaining of sharp images of the geographic rainfall pattern. Widespread systems were undetected over low-rainfall areas such as regions off Peru. Conversely, infrequent large systems brought a significant percentage of rainfall over semiarid tropics such as the Sahel. This demonstrated an increased need for regional sampling of extreme phenomena. Differences in data collected over a period of 16 yr were used to examine sampling adequacy. The results indicated that more than 10% of the 0.1°-scale sampling error accounted for half of the TRMM domain even for a 10-yr data accumulation period. Rainfall at the 0.1° scale was negatively biased in the first few years for over more than half of the areas because of a lack of high-impact samples. The areal fraction of the 0.1°-scale climatology with a 50% accuracy exceeded 95% in the ninth year and in the fifth year for those areas with rainfall >2 mm day−1. A monotonic increase in the degree of similarity of finescale rainfall to the best estimate with an accuracy of 10% illustrated the need for further sampling.

As the Japanese Global Precipitation Measurement (GPM) product, the Global Satellite Mapping of Precipitation (GSMaP) has been provided by the Japan Aerospace Exploration Agency (JAXA) to distribute hourly global precipitation map with 0.1° × 0.1° lat/lon grid. Since JAXA started near-real-time processing of the GSMaP on November 2007, there have been various significant improvements to the GSMaP. This paper summarizes GSMaP products and related algorithms in the GPM era and shows validation results in Japan and the United States.

The estimation of precipitation (rainfall and snowfall) across the Earth’s surface is important for both science and user applications, ranging from understanding and improving our knowledge of the global energy and water cycle, to water resources and hydrological modelling, and to societal applications such as water availability and monitoring of waterborne diseases (see Kirschbaum DB, Huffman GJ, Adler RF, Braun S, Garrett K, Jones E, McNally A, Skofronick-Jackson G, Stocker E, Wu H, Zaitchik BF, Bull Am Meteorol Soc 98:1169–1194, 2017). The global mapping of precipitation through conventional means is essentially limited to land areas due to the reliance upon rain (and snow) gauges and/or radar (see Kidd C, Becker A, Huffman GJ, Muller CL, Joe P, Skofronick-Jackson G, Kirschbaum DB, Bull Am Meteorol Soc 98:69–78, 2017a). For truly global precipitation mapping satellite observations must be used. A range of techniques, algorithms and schemes have been developed to exploit these satellite observations and generate quantitative precipitation products, many with (quasi-) global coverage. Alongside these techniques, there is a need for the inter-comparison, verification, and validation of such products in order to quantify their accuracy and performance (and consistency) for both developers and users. The International Precipitation Working Group (IPWG) has supported a long-term effort to inter-compare and validate precipitation products through the exploitation of large-scale regional surface reference data sets. Here, we present the current and future validation efforts of the IPWG together with examples of satellite-surface inter-comparisons.

Over coastal mountain ranges of the Asian monsoon region, heavy orographic rainfall is frequently associated with low precipitation-top heights (PTHs). This leads to conspicuous underestimation of rainfall using microwave radiometer algorithms, which conventionally assume that heavy rainfall is associated with high PTHs. Although topographically forced upward motion is important for rainfall occurrence, it does not fully constrain precipitation profiles in this region. This paper focuses on the thermodynamic characteristics of the atmosphere that determine PTHs in tropical coastal mountains of Asia (Western Ghats, Arakan Yoma, Bilauktaung, Cardamom, Annam Range, and the Philippines). PTHs of heavy orographic rainfall generally decrease with enhanced low- and midlevel relative humidity, especially during the summer monsoon. In contrast, PTHs over the Annam Range of the Indochina Peninsula increase with enhanced low-level and midlevel relative humidity during the transition from boreal summer to winter monsoon, demonstrating that convection depth is not simply a function of humidity. Instead, PTHs of heavy orographic rainfall decreased with increasing low-level stability for all monsoon regions considered in this study, as well as the Annam Range during the transition from boreal summer to winter monsoon. Therefore, low-level static stability, which inhibits cloud growth and promotes cloud detrainment, appears to be the most important parameter in determining PTHs of heavy rainfall in the Asian monsoon region.

The rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land–ocean–coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions.The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land–ocean–coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region.

In a high-resolution atmospheric model, the terrain is resolved in detail, and thus steeper and more complex terrain can be represented. Thus, we developed an atmospheric model that represents the terrain by using the Chimera grid method, which represents the computational region as a composite of overlapping grids. To test this model, we simulated a lee wave over a semicircular mountain and one over a tall semi-elliptical mountain. The results show that the Chimera grid method can be used to represent a very steep terrain which terrain-following coordinates and numerically generated coordinates cannot represent appropriately.

We incorporate an orographic/nonorographic rainfall classification scheme into the Global Satellite Mapping of Precipitation algorithm for passive microwave radiometers. It improves rainfall estimation over the entire Asian region. However, low verification scores over the United States and Mexico result because vertical profiles of rainfall over the Sierra Madre Mountains are high even for orographic rainfall conditions. In this region, lightning activity is vigorous, with large amounts of solid particles such as graupels, occurring with strong convections. Hence, the orographic/nonorographic rainfall classification scheme is switched off for regions where strong lightning activity occurs in the rainfall type database. The revised zonal mean rainfall amounts obtained from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager are now in better agreement with those from the version 7 of the TRMM Precipitation Radar over the United States and Mexico, as well as Asia.

A new rain/no-rain classification (RNC) method over land for microwave sounders is proposed based upon evaluations using matched-up cases of NOAA Advanced Microwave Sounding Unit (AMSU) and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The performance of the original RNC method over land for the Global Satellite Mapping of Precipitation (GSMaP) algorithm was compared with PR observations. In the precipitation systems observed by the PR, light rain regions were missed by the original method. The precipitation area derived from the scattering signature at 150 GHz was larger than that derived from the scattering signature at 89 GHz, and therefore the revised RNC method employs the Scattering Index based upon a combination of 89 and 150 GHz. As a result, areas of light rain that had storm heights of 3.52 km, on average, were detected by the revised method. For comparison with the NOAA operational Microwave Surface and Precipitation Products System (MSPPS), because most shallow precipitation was missed by the original method, the POD and TS of the original method were considerably lower than those of the MSPPS, although the FAR of the original method was better. Conversely, because the detection of shallow precipitation was improved by the revised method, although the FAR of the revised method was similar to that of the MSPPS, the POD and TS of the revised method were improved and the average skill scores were slightly better than those of the MSPPS.

Latent heating (LH) from precipitation systems with different sizes, depths, and convective intensities is quantified with 15 years of LH retrievals from version 7 Precipitation Radar (PR) products of the Tropical Rainfall Measuring Mission (TRMM). Organized precipitation systems, such as mesoscale convective systems (MCSs; precipitation area > 2000 km2), contribute to 88% of the LH above 7 km over tropical land and 95% over tropical oceans. LH over tropical land is mainly from convective precipitation, and has one vertical mode with a peak from 4 to 7 km. There are two vertical modes of LH over tropical oceans. The shallow mode from about 1 to 4 km results from small, shallow, and weak precipitation systems, and partially from congestus clouds with radar echo top between 5 and 8 km. The deep mode from 5 to 9 km is mainly from stratiform precipitation in MCSs. MCSs of different regions and seasons have different LH vertical structure mainly due to the different proportion of stratiform precipitation. MCSs over ocean have a larger fraction of stratiform precipitation and a top-heavy LH structure. MCSs over land have a higher percentage of convective versus stratiform precipitation, which results in a relatively lower-level peak in LH compared to MCSs over the ocean. MCSs during monsoons have properties of LH in between those typical land and oceanic MCSs. Consistent with the diurnal variation of precipitation, tropical land has a stronger LH diurnal variation than tropical oceans with peak LH in the late afternoon. Over tropical oceans in the early morning, the shallow mode of LH peaks slightly earlier than the deep mode. There are almost no diurnal changes of MCSs LH over oceans. However, the small convective systems over land contribute a significant amount of LH at all vertical levels in the afternoon, when the contribution of MCSs is small.

Heavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original GSMaP algorithm.

The Madden-Julian Oscillation (MJO) is the dominant intraseasonal signal in the global tropical atmosphere. Almost all numerical climate models have difficulty to simulate realistic MJO. Four TRMM datasets of latent heating were diagnosed for signals in the MJO. In all four datasets, vertical structures of latent heating are dominated by two components, one deep with its peak above the melting level and one shallow with its peak below. Profiles of the two components are nearly ubiquitous in longitude, allowing a separation of the vertical and zonal/temporal variations when the latitudinal dependence is not considered. All four datasets exhibit robust MJO spectral signals in the deep component as eastward propagating spectral peaks centered at period of 50 days and zonal wavenumber 1, well distinguished from lower- and higher-frequency power and much stronger than the corresponding westward power. The shallow component shows similar but slightly less robust MJO spectral peaks. MJO signals were further extracted from a combination of band-pass (30 - 90 day) filtered deep and shallow components. Largest amplitudes of both deep and shallow components of the MJO are confined to the Indian and western Pacific Oceans. There is a local minimum in the deep components over the Maritime Continent. The shallow components of the MJO differ substantially among the four TRMM datasets in their detailed zonal distributions in the eastern hemisphere. In composites of the heating evolution through the life cycle of the MJO, the shallow components lead the deep ones in some datasets and at certain longitudes. In many respects, the four TRMM datasets agree well in their deep components, but not in their shallow components and the phase relations between the deep and shallow components. These results indicate that caution must be exercised in applications of these latent heating data.

A K-means clustering algorithm was used to classify Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) scenes within 1° square patches over the tropical (15°S–15°N) oceans. Three cluster centroids or “regimes” that minimize the Euclidean distance metric in a five-dimensional space of standardized variables were sought [convective surface rainfall rate; ratio of convective rain to total rain; and fractions of convective echo profiles with tops in three fixed height ranges (9 km)]. Independent cluster computations in adjacent ocean basins return very similar clusters in terms of PR echo-top distributions, rainfall, and diabatic heating profiles. The clusters consist of shallow convection (SHAL cluster), with a unimodal distribution of PR echo tops and composite diabatic heating rates of ∼2 K day−1 below 3 km; midlevel convection (MID-LEV cluster), with a bimodal distribution of PR echo tops and ∼5 K day−1 heating up to about 7 km; and deeper convection (DEEP cluster), with a multimodal distribution of PR echo tops and >20 K day−1 heating from 5 to 10 km. Each contributes roughly 20%–40% in terms of total tropical rainfall, but with MID-LEV clusters especially enhanced in the Indian and Atlantic sectors, SHAL relatively enhanced in the central and east Pacific, and DEEP most prominent in the western Pacific. While the clusters themselves are quite similar in rainfall and heating, specific cloud types defined according to the PR echo top and surface rainfall rate are less similar and exhibit systematic differences from one cluster to another, implying that the degree to which precipitation structures are similar decreases when one considers individual precipitating clouds as repeating tropical structures instead of larger-scale cluster ensembles themselves.

The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of its formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the Tropics. Associated with this rainfall is a vast amount of heat, which is known as latent heat. It arises mainly due to the phase change of water vapor condensing into liquid droplets; three-fourths of the total heat energy available to the Earth's atmosphere comes from tropical rainfall. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. Three-dimensional distributions of latent heating estimated from Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR)utilizing the Spectral Latent Heating (SLH) algorithm are analyzed. Mass-weighted and vertically integrated latent heating averaged over the tropical oceans is estimated as approx.72.6 J/s (approx.2.51 mm/day), and that over tropical land is approx.73.7 J/s (approx.2.55 mm/day), for 30degN-30degS. It is shown that non-drizzle precipitation over tropical and subtropical oceans consists of two dominant modes of rainfall systems, deep systems and congestus. A rough estimate of shallow mode contribution against the total heating is about 46.7 % for the average tropical oceans, which is substantially larger than 23.7 % over tropical land. While cumulus congestus heating linearly correlates with the SST, deep mode is dynamically bounded by large-scale subsidence. It is notable that substantial amount of rain, as large as 2.38 mm day-1 in average, is brought from congestus clouds under the large-scale subsiding circulation. It is also notable that even in the region with SST warmer than 28 oC, large-scale subsidence effectively suppresses the deep convection, remaining the heating by congestus clouds. Our results support that the entrainment of mid-to-lower-tropospheric dry air, which accompanies the large-scale subsidence is the major factor suppressing the deep convection. Therefore, representation of the realistic entrainment is very important for proper reproduction of precipitation distribution and resultant large-scale circulation.

The relationship among surface rainfall, its intensity, and its associated stratiform amount is established by examining observed precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The results show that for moderate–high stratiform fractions, rain probabilities are strongly skewed toward light rain intensities. For convective-type rain, the peak probability of occurrence shifts to higher intensities but is still significantly skewed toward weaker rain rates. The main differences between the distributions for oceanic and continental rain are for heavily convective rain. The peak occurrence, as well as the tail of the distribution containing the extreme events, is shifted to higher intensities for continental rain. For rainy areas sampled at 0.5° horizontal resolution, the occurrence of conditional rain rates over 100 mm day−1 is significantly higher over land. Distributions of rain intensity versus stratiform fraction for simulated precipitation data obtained from cloud-resolving model (CRM) simulations are quite similar to those from the satellite, providing a basis for mapping simulated cloud quantities to the satellite observations. An improved convective–stratiform heating (CSH) algorithm is developed based on two sources of information: gridded rainfall quantities (i.e., the conditional intensity and the stratiform fraction) observed from the TRMM PR and synthetic cloud process data (i.e., latent heating, eddy heat flux convergence, and radiative heating/cooling) obtained from CRM simulations of convective cloud systems. The new CSH algorithm-derived heating has a noticeably different heating structure over both ocean and land regions compared to the previous CSH algorithm. Major differences between the new and old algorithms include a significant increase in the amount of low- and midlevel heating, a downward emphasis in the level of maximum cloud heating by about 1 km, and a larger variance between land and ocean in the new CSH algorithm.

This study aims to evaluate the consistency and discrepancies in estimates of diabatic heating profiles associated with precipitation based on satellite observations and microphysics and those derived from the thermodynamics of the large-scale environment. It presents a survey of diabatic heating profile estimates from four Tropical Rainfall Measuring Mission (TRMM) products, four global reanalyses, and in situ sounding measurements from eight field campaigns at various tropical locations. Common in most of the estimates are the following: (i) bottom-heavy profiles, ubiquitous over the oceans, are associated with relatively low rain rates, while top-heavy profiles are generally associated with high rain rates; (ii) temporal variability of latent heating profiles is dominated by two modes, a deep mode with a peak in the upper troposphere and a shallow mode with a low-level peak; and (iii) the structure of the deep modes is almost the same in different estimates and different regions in the tropics. The primary uncertainty is in the amount of shallow heating over the tropical oceans, which differs substantially among the estimates.

The spectral latent heating (SLH) algorithm was developed to estimate latent heating profiles for the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR). The method uses TRMM PR information (precipitation-top height, precipitation rates at the surface and melting level, and rain type) to select heating profiles from lookup tables (LUTs). LUTs for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) using a cloud-resolving model (CRM). The two-dimensional (2D) CRM was used in previous studies. The availability of exponentially increasing computer capabilities has resulted in three-dimensional (3D) CRM simulations for multiday periods becoming increasingly prevalent. In this study, LUTs from the 2D and 3D simulations are compared. Using the LUTs from 3D simulations results in less agreement between the SLH-retrieved heating and sounding-based heating for the South China Sea Monsoon Experiment (SCSMEX). The level of SLH-estimated maximum heating is lower than that of the sounding-derived maximum heating. This is explained by the fact that using the 3D LUTs results in stronger convective heating and weaker stratiform heating above the melting level than is the case if using the 2D LUTs. More condensate is generated in and carried from the convective region in the 3D model than in the 2D model, and less condensate is produced by the stratiform region’s own upward motion.

We develop an over-ocean rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) based on the Global Satellite Mapping of Precipitation (GSMaP) microwave radiometer algorithm. This algorithm combines an emission-based estimate from brightness temperature (Tb) at 23 GHz and a scattering-based estimate from Tb at 89 GHz, depending on a scattering index (SI) computed from Tb at both 89 and 150 GHz. Precipitation inhomogeneities are also taken into account. The GSMaP-retrieved rainfall from the AMSU (GSMaP_AMSU) is compared with the National Oceanic and Atmospheric Administration (NOAA) standard algorithm (NOAA_AMSU)-retrieved data using Tropical Rainfall Measuring Mission (TRMM) data as a reference. Rain rates retrieved by GSMaP_AMSU have better agreement with TRMM estimates over midlatitudes during winter. Better estimates over multitudes over winter are given by the use of Tb at 23 GHz in the GSMaP_AMSU algorithm. It was also shown that GSMaP_AMSU has higher rain detection than NOAA_AMSU.

This paper describes a precipitation-retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) that was developed under the Global Satellite Mapping of Precipitation project (GSMaP) by improving the authors' previous algorithm. The basic idea of the GSMaP algorithm is to find the optimal precipitation for which the brightness temperatures (TBs) calculated by the radiative-transfer model (RTM) fit best with the observed TBs. The main improvements of the GSMaP algorithm over the authors' previous work are as follows: (1) use of precipitation-related variable models (precipitation profiles, drop-size distribution, etc.) and precipitation detection and inhomogeneity estimation methods based on TRMM observation studies; (2) use of scattering signals of the TMI Polarization-Corrected Temperature (PCT) at 37 and 85 GHz (PCT37, PCT85) and scattering-signal correction for tall precipitation (thickness between precipitation top level and freezing level (Dtop) larger than 6 km) over land and coastal areas. In order to validate the GSMaP algorithm, we compared its retrievals from TMI TBs in 1998 with the TRMM Precipitation Radar (PR) and Goddard Profiling Algorithm (GPROF) retrievals (2A12 version 6). The results show that (1) over land and coastal areas, the GSMaP retrievals agreed better with PR than GPROF for tall precipitation (Dtop>4 km) weaker than 10 mm h-1, while both GSMaP and GPROF underestimated PR precipitation rates for precipitation heavier than 10 mm h-1; (2) over ocean, the GSMaP retrievals agreed better with PR than GPROF for precipitation heavier than 10 mm h-1, while GSMaP slightly overestimated precipitation weaker than 10 mm h-1 compared to PR and GPROF; (3) The GSMaP algorithm failed to detect some precipitation areas weaker than 2 mm h-1 over sub-tropical oceans. Experimental algorithms with different precipitation-related variable models and retrieval methods using scattering signals were applied to TMI TBs in July 1998 to examine the effect of the above improvements to the GSMaP algorithm. The results show that the improvement of the precipitation profile alleviated the underestimation of precipitation heavier than 10 mm h-1 over land and coastal areas, that the combined use of new physical-related variable models alleviated the underestimation of precipitation heavier than 10 mm h-1 over ocean, and that the use of PCT37 and scattering-signal correction reduced the overestimation of tall precipitation (Dtop>4 km) weaker than 10 mm h-1 over land and coastal areas.

Nonuniform beamfilling (NUBF) is a major error source in physical retrieval algorithms for estimating rain rates using satellite-borne passive microwave radiometers. The NUBF effects over the ocean within the beam effective field of view in the 10 GHz channel (FOV10) for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are investigated from simultaneous measurements made by the TMI and the Precipitation Radar (PR) aboard the TRMM satellite. They are investigated with respect to variability of a non-uniform parameter defined by the lognormal assumption from January to December 2000. The parameter computed using surface rain rates estimated from PR data tends to be small (large) for stratiform (convective) rainfall in the FOV10. The parameter computed using surface rain rates estimated from brightness temperature (Tb) at 85 GHz is systematically lower in stratiform rainfall than the reference derived by the PR. Systematic differences are not found for the convective cases. To evaluate the effects on rain retrievals due to the systematic differences of the non-uniform parameters, a simple adjustment is applied, and rain retrievals from the observed TMI Tb at 10 GHz vertical polarization are performed over the ocean. Relatively large increases in rain rates retrieved using corrected parameters are found in the tropics, while the increases of the corrected parameters are similar for all latitudes. Effects of the non-uniform parameter differences on the rain retrievals are nonlinear and could be closely related to high background values of the non-uniform parameter in the tropics and more frequent high rainfall intensities.

Global rainfall products of high spatial and temporal resolutions have been provided using combined data from passive microwave (PMW) sensors in low Earth orbit and infrared (IR) radiometers in geostationary Earth orbit (GEO). This study compared six satellite rainfall estimates around Japan with reference to a ground-radar dataset calibrated by rain gauges provided by the Japan Meteorological Agency (JMA) from January through December 2004. Validation results tended to be better for the products with temporal interpolation based upon the morphed technique using GEO IR information. Satellite estimates were poor for light rainfall during the warm season and for very heavy rainfall. Further analyses of satellite estimates were conducted in terms of data sources and surface types. Effective performance by the merger of PMW sounders over the ocean was verified by radar validation, in addition to the best results of the PMW imagers. Overall, validation results over the ocean were best, and results over mountainous regions were worst. Performance was poor over coasts and small islands, due to the problem of PMW retrievals. This study focused on hydrometeor profiles of orographic heavy rainfall over the Japanese Archipelago, which could be related to the poor performance of satellite estimates in very heavy rainfall.

A system has been developed and implemented that integrates passive microwave radiometer data with infrared radiometer data in order to have high temporal (1 hour) and spatial (0.1 degree) resolution global precipitation estimates. The product (GSMaP_MVK) is produced based on a Kalman filter model that refines the precipitation rate propagated based on the atmospheric moving vector derived from two successive IR images. The proposed method was evaluated and compared with other high-resolution precipitation products and the ground-based data collected by the Automated Meteorological Data Acquisition System (AMeDAS) near Japan. It was clearly shown that the approach described in this paper performed better than without the Kalman filter, and the time series of the hourly global precipitation pattern demonstrated the potential capabilities for weather monitoring and typhoon tracking. The GSMaP_MVK product achieved a score comparable to the CMORPH and the 3B42RT products.