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Robert F. Adler
,
Christian Kummerow
,
David Bolvin
,
Scott Curtis
, and
Chris Kidd

Abstract

Three years of Tropical Rainfall Measuring Mission (TRMM) monthly estimates of tropical surface rainfall are analyzed to document and understand the differences among the TRMM-based estimates and how these differences relate to the pre-TRMM estimates and current operational analyses. Variation among the TRMM estimates is shown to be considerably smaller than among a pre-TRMM collection of passive microwave-based products. Use of both passive and active microwave techniques in TRMM should lead to increased confidence in converged estimates.

Current TRMM estimates are shown to have a range of about 20% for the tropical ocean as a whole, with variations in heavily raining ocean areas of the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ) having differences over 30%. In midlatitude ocean areas the differences are smaller. Over land there is a distinct difference between the Tropics and midlatitude with a reversal between some of the products as to which tends to be relatively high or low. Comparisons of TRMM estimates with ocean atoll and land rain gauge information point to products that might have significant regional biases. The bias of the radar-based product is significantly low compared with atoll rain gauge data, while the passive microwave product is significantly high compared to rain gauge data in the deep Tropics.

The evolution of rainfall patterns during the recent change from intense El Niño to a long period of La Niña and then a gradual return to near neutral conditions is described using TRMM. The time history of integrated rainfall over the tropical oceans (and land) during this period differs among the passive and active microwave TRMM estimates.

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Lindsey J. M. Hayden
,
Jackson Tan
,
David T. Bolvin
, and
George J. Huffman

Abstract

The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing, highly localized effects. The diurnal cycle in regions such as the Amazon and the Maritime Continent are of particular interest, due to the complex coastal and terrain effects. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset are used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-yr (2000–18) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on half-hourly averaged precipitation at 0.1° × 0.1°. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broadscale patterns of diurnal variability but does not capture all the fine-scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long-record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.

Open access
Robert F. Adler
,
George J. Huffman
,
David T. Bolvin
,
Scott Curtis
, and
Eric J. Nelkin

Abstract

A technique is described to use Tropical Rainfall Measuring Mission (TRMM) combined radar–radiometer information to adjust geosynchronous infrared satellite data [the TRMM Adjusted Geostationary Operational Environmental Satellite Precipitation Index (AGPI)]. The AGPI is then merged with rain gauge information (mostly over land) to provide finescale (1° latitude × 1° longitude) pentad and monthly analyses, respectively. The TRMM merged estimates are 10% higher than those from the Global Precipitation Climatology Project (GPCP) when integrated over the tropical oceans (37°N–37°S) for 1998, with 20% differences noted in the most heavily raining areas. In the dry subtropics the TRMM values are smaller than the GPCP estimates. The TRMM merged product tropical-mean estimates for 1998 are 3.3 mm day−1 over ocean and 3.1 mm day−1 over land and ocean combined. Regional differences are noted between the western and eastern Pacific Ocean maxima when TRMM and GPCP are compared. In the eastern Pacific rain maximum the TRMM and GPCP mean values are nearly equal, which is very different from the other tropical rainy areas where TRMM merged product estimates are higher. This regional difference may indicate that TRMM is better at taking into account the vertical structure of the rain systems and the difference in structure between the western and eastern (shallower) Pacific convection.

Comparisons of these TRMM merged analysis estimates with surface datasets shows varied results; the bias is near zero when compared with western Pacific Ocean atoll rain gauge data, but is significantly positive as compared with Kwajalein radar estimates (adjusted by rain gauges). Over land the TRMM estimates also show a significant positive bias. The inclusion of gauge information in the final merged product significantly reduces the bias over land, as expected.

The monthly precipitation patterns produced by the TRMM merged data process clearly show the evolution of the El Niño–Southern Oscillation (ENSO) tropical precipitation pattern from early 1998 (El Niño) to early 1999 (La Niña) and beyond. The El Niño-minus-La Niña difference map shows the expected eastern Pacific maximum, the “Maritime Continent” minima, and other tropical and midlatitude features, very similar to those detected by the GPCP analyses. However, summing the El Niño-minus-La Niña differences over the global tropical oceans yields divergent answers for interannual changes from TRMM, GPCP, and other estimates. This emphasizes the need for additional validation and analysis before it is feasible to understand the relations between global precipitation anomalies and Pacific Ocean ENSO temperature changes.

Full access
Jackson Tan
,
George J. Huffman
,
David T. Bolvin
, and
Eric J. Nelkin

Abstract

As the U.S. Science Team’s globally gridded precipitation product from the NASA–JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1° every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60°N–60°S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.

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Jian-Jian Wang
,
Robert F. Adler
,
George J. Huffman
, and
David Bolvin

Abstract

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primary TRMM instruments: the TRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation σ among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3 mm day−1 for the deep tropical ocean belt between 10°N and 10°S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by σ is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (~16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

Full access
David T. Bolvin
,
George J. Huffman
,
Eric J. Nelkin
, and
Jackson Tan

Abstract

Satellite-based precipitation estimates provide valuable information where surface observations are not readily available, especially over the large expanses of the ocean where in situ precipitation observations are very sparse. This study compares monthly precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG) with gauge observations from 37 low-lying atolls from the Pacific Rainfall Database for the period June 2000–August 2020. Over the analysis period, IMERG estimates are slightly higher than the atoll observations by 0.67% with a monthly correlation of 0.68. Seasonally, DJF shows excellent agreement with a near-zero bias, while MAM shows IMERG is low by 4.6%, and JJA is high by 1.2%. SON exhibits the worst performance, with IMERG overestimating by 6.5% compared to the atolls. The seasonal correlations are well contained in the range 0.67–0.72, with the exception of SON at 0.62. Furthermore, SON has the highest RMSE at 4.70 mm day−1, making it the worst season for all metrics. Scatterplots of IMERG versus atolls show IMERG, on average, is generally low for light precipitation accumulations and high for intense precipitation accumulations, with best agreement at intermediate rates. Seasonal variations exist at light and intermediate rate accumulations, but IMERG consistently overestimates at intense precipitation rates. The differences between IMERG and atolls vary over time but do not exhibit any discernable trend or dependence on atoll population. The PACRAIN atoll gauges are not wind-loss corrected, so application of an appropriate adjustment would increase the precipitation amounts compared to IMERG. These results provide useful insight to users as well as valuable information for future improvements to IMERG.

Free access
Jackson Tan
,
George J. Huffman
,
David T. Bolvin
,
Eric J. Nelkin
, and
Manikandan Rajagopal

Abstract

A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and a decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood), which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

Full access
David T. Bolvin
,
Robert F. Adler
,
George J. Huffman
,
Eric J. Nelkin
, and
Jani P. Poutiainen

Abstract

Monthly and daily products of the Global Precipitation Climatology Project (GPCP) are evaluated through a comparison with Finnish Meteorological Institute (FMI) gauge observations for the period January 1995–December 2007 to assess the quality of the GPCP estimates at high latitudes. At the monthly scale both the final GPCP combination satellite–gauge (SG) product is evaluated, along with the satellite-only multisatellite (MS) product. The GPCP daily product is scaled to sum to the monthly product, so it implicitly contains monthly-scale gauge influence, although it contains no daily gauge information. As expected, the monthly SG product agrees well with the FMI observations because of the inclusion of limited gauge information. Over the entire analysis period the SG estimates are biased low by 6% when the same wind-loss adjustment is applied to the FMI gauges as is used in the SG analysis. The interannual anomaly correlation is about 0.9. The satellite-only MS product has a lesser, but still reasonably good, interannual correlation (∼0.6) while retaining a similar bias due to the use of a climatological bias adjustment. These results indicate the value of using even a few gauges in the analysis and provide an estimate of the correlation error to be expected in the SG analysis over ocean and remote land areas where gauges are absent. The daily GPCP precipitation estimates compare reasonably well at the 1° latitude × 2° longitude scale with the FMI gauge observations in the summer with a correlation of 0.55, but less so in the winter with a correlation of 0.45. Correlations increase somewhat when larger areas and multiday periods are analyzed. The day-to-day occurrence of precipitation is captured fairly well by the GPCP estimates, but the corresponding precipitation event amounts tend to show wide variability. The results of this study indicate that the GPCP monthly and daily fields are useful for meteorological and hydrological studies but that there is significant room for improvement of satellite retrievals and analysis techniques in this region. It is hoped that the research here provides a framework for future high-latitude assessment efforts such as those that will be necessary for the upcoming satellite-based Global Precipitation Measurement (GPM) mission.

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Phu Nguyen
,
Mohammed Ombadi
,
Vesta Afzali Gorooh
,
Eric J. Shearer
,
Mojtaba Sadeghi
,
Soroosh Sorooshian
,
Kuolin Hsu
,
David Bolvin
, and
Martin F. Ralph

Abstract

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Open access
Scott Curtis
,
Robert F. Adler
,
George J. Huffman
,
Guojun Gu
,
David T. Bolvin
, and
Eric J. Nelkin
Full access