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  • Author or Editor: Yukari N. Takayabu x
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Atsushi Hamada and Yukari N. Takayabu

Abstract

This study reports on the presence of suspicious “extreme rainfall” data in the 2A25 version-7 (V7) product of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) dataset and introduces a simple method for detecting and filtering out the suspicious data. These suspicious data in V7 are found by comparing the extreme rainfall characteristics in the V7 and version-6 products. Most of the suspicious extremes are located over land, especially in mountainous regions. Radar reflectivities in the suspicious extremes show significant monotonic increases toward the echo bottom. These facts indicate that the suspicious extremes are mainly caused by contamination from ground or sea clutter. A simple thresholding filter for eliminating the suspicious extreme data is developed using common characteristics in the horizontal and vertical rainfall structures and reflectivities in the suspicious extremes. The proposed filter mitigates deformations in the frequency distribution of the surface rainfall rate in the 2A25 V7 product.

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Shoichi Shige, Yukari N. Takayabu, and Wei-Kuo Tao

Abstract

The spectral latent heating (SLH) algorithm was developed to estimate apparent heat source (Q 1) profiles for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Parts I and II of this study. In this paper, the SLH algorithm is used to estimate apparent moisture sink (Q 2) profiles. The procedure of Q 2 retrieval is the same as that of heating retrieval except for using the Q 2 profile lookup tables derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) utilizing a cloud-resolving model (CRM). The Q 2 profiles were reconstructed from CRM-simulated parameters with the COARE table and then compared with CRM-simulated “true” Q 2 profiles, which were computed directly from the water vapor equation in the model. The consistency check indicates that discrepancies between the SLH-reconstructed and CRM-simulated profiles for Q 2, especially at low levels, are larger than those for Q 1 and are attributable to moistening for the nonprecipitating region that SLH cannot reconstruct. Nevertheless, the SLH-reconstructed total Q 2 profiles are in good agreement with the CRM-simulated ones. The SLH algorithm was applied to PR data, and the results were compared with Q 2 profiles derived from the budget study. Although discrepancies between the SLH-retrieved and sounding-based profiles for Q 2 for the South China Sea Monsoon Experiment (SCSMEX) are larger than those for heating, key features of the vertical profiles agree well. The SLH algorithm can also estimate differences of Q 2 between the western Pacific Ocean and the Atlantic Ocean, consistent with the results from the budget study.

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Shoichi Shige, Yukari N. Takayabu, Wei-Kuo Tao, and Chung-Lin Shie

Abstract

The spectral latent heating (SLH) algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Part I of this study. The method uses PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables 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) utilizing a cloud-resolving model (CRM). To assess its global application to TRMM PR data, the universality of the lookup tables from the TOGA COARE simulations is examined in this paper. Heating profiles are reconstructed from CRM-simulated parameters (i.e., PTH, precipitation rates at the surface and melting level, and rain type) and are compared with the true CRM-simulated heating profiles, which are computed directly by the model thermodynamic equation. CRM-simulated data from the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), South China Sea Monsoon Experiment (SCSMEX), and Kwajalein Experiment (KWAJEX) are used as a consistency check. The consistency check reveals discrepancies between the SLH-reconstructed and Goddard Cumulus Ensemble (GCE)-simulated heating above the melting level in the convective region and at the melting level in the stratiform region that are attributable to the TOGA COARE table. Discrepancies in the convective region are due to differences in the vertical distribution of deep convective heating due to the relative importance of liquid and ice water processes, which varies from case to case. Discrepancies in the stratiform region are due to differences in the level separating upper-level heating and lower-level cooling. Based on these results, improvements were made to the SLH algorithm. Convective heating retrieval is now separated into upper-level heating due to ice processes and lower-level heating due to liquid water processes. In the stratiform region, the heating profile is shifted up or down by matching the melting level in the TOGA COARE lookup table with the observed one. Consistency checks indicate the revised SLH algorithm performs much better for both the convective and stratiform components than does the original one. The revised SLH algorithm was applied to PR data, and the results were compared with heating profiles derived diagnostically from SCSMEX sounding data. Key features of the vertical profiles agree well—in particular, the level of maximum heating. The revised SLH algorithm was also applied to PR data for February 1998 and February 1999. The results are compared with heating profiles derived by the convective–stratiform heating (CSH) algorithm. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm.

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Masafumi Hirose, Yukari N. Takayabu, Atsushi Hamada, Shoichi Shige, and Munehisa K. Yamamoto

Abstract

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.

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Shoichi Shige, Yukari N. Takayabu, Wei-Kuo Tao, and Daniel E. Johnson

Abstract

An algorithm, the spectral latent heating (SLH) algorithm, has been developed to estimate latent heating profiles for the Tropical Rainfall Measuring Mission precipitation radar with a cloud-resolving model (CRM). Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were produced with numerical simulations of tropical cloud systems in the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. For convective and shallow stratiform regions, the lookup table refers to the precipitation-top height (PTH). For the anvil region, on the other hand, the lookup table refers to the precipitation rate at the melting level instead of PTH. A consistency check of the SLH algorithm was also done with the CRM-simulated outputs. The first advantage of this algorithm is that differences of heating profiles between the shallow convective stage and the deep convective stage can be retrieved. This is a result of the utilization of observed information, not only on precipitation type and intensity, but also on the precipitation depth. The second advantage is that heating profiles in the decaying stage with no surface rain can also be retrieved. This comes from utilization of the precipitation rate at the melting level for anvil regions.

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Satoru Yokoi, Yukari N. Takayabu, Kazuaki Nishii, Hisashi Nakamura, Hirokazu Endo, Hiroki Ichikawa, Tomoshige Inoue, Masahide Kimoto, Yu Kosaka, Takafumi Miyasaka, Kazuhiro Oshima, Naoki Sato, Yoko Tsushima, and Masahiro Watanabe

Abstract

The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.

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