The Eastern Pacific ITCZ during the Boreal Spring

Abstract The 6-yr (1998–2003) rainfall products from the Tropical Rainfall Measuring Mission (TRMM) are used to quantify the intertropical convergence zone (ITCZ) in the eastern Pacific (defined by longitudinal averages over 90°–130°W) during boreal spring (March–April). The double-ITCZ phenomenon, represented by the occurrence of two maxima with respect to latitude in monthly mean rainfall, is observed in most but not all of the years studied. The relative spatial locations of maxima in sea surface temperature (SST), rainfall, and surface pressure are examined. Interannual and weekly variability are characterized in SST, rainfall, surface convergence, total column water vapor, and cloud water. There appears to be a competition for rainfall between the two hemispheres during this season. When one of the two rainfall maxima is particularly strong, the other tends to be weak, with the total rainfall integrated over the two varying less than does the difference between the rainfall integrated over each separately. There is some evidence for a similar competition between the SST maxima in the two hemispheres, but this is more ambiguous, and there is evidence that some variations in the relative strengths of the two rainfall maxima may be independent of SST. Using a 25-yr (1979–2003) monthly rainfall dataset from the Global Precipitation Climatology Project (GPCP), four distinct ITCZ types during March–April are defined, based on the relative strengths of rainfall peaks north and south of, and right over, the equator. Composite meridional profiles and spatial distributions of rainfall and SST are documented for each type. Consistent with previous studies, an equatorial cold tongue is essential to the existence of the double ITCZs. However, too strong a cold tongue may dampen either the southern or northern rainfall maximum, depending on the magnitude of SST north of the equator.

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Observed Characteristics of the MJO Relative to Maximum Rainfall

Journal of the Atmospheric Sciences ◽

10.1175/jas3968.1 ◽

2007 ◽

Vol 64 (7) ◽

pp. 2332-2354 ◽

Cited By ~ 228

Author(s):

James J. Benedict ◽

David A. Randall

Keyword(s):

Deep Convection ◽

Lower Troposphere ◽

Geographic Location ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Convective Transport ◽

Maximum Rainfall ◽

Tropospheric Heating ◽

Cumulus Congestus ◽

Global Precipitation

Abstract This study examines various dynamical and thermodynamical processes that characterize the Madden–Julian oscillation (MJO). Episodes of deep convection related to the MJO based on rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite and the Global Precipitation Climatology Project (GPCP) are identified. Although broad convective envelopes are located utilizing spectrally filtered precipitation, analyses of the features within the envelopes are carried out using unfiltered rainfall and 40-yr ECMWF Re-Analysis (ERA-40) fields. The events are composited and categorized based on geographic location and relative intensity. The composited fields illustrate that, prior to the onset of deep convection, shallow cumulus and cumulus congestus clouds are actively involved in vertical convective transport of heat and moisture. Drying, first accomplished immediately following deep convection in the lower troposphere, is associated with an enhanced horizontal (westerly) advective component and may be related to mesoscale processes. Drying related to deep-layer subsidence is delayed until one to two weeks following intense rainfall. The importance of gradual lower-tropospheric heating and moistening and the vertical transport of energy and moisture are shown in a comparison of vigorous and weak MJO events. Additionally, a comparison of the composite fields to proposed wave instability theories suggests that certain theories are effective in explaining specific phases of the disturbance, but no single theory can yet explain all aspects of the MJO. The discharge–recharge and frictional moisture convergence mechanisms are most relevant for explaining many of the observed features of MJO evolution.

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Uncertainty Assessments of Satellite Derived Rainfall Products

10.20944/preprints201611.0019.v1 ◽

2016 ◽

Cited By ~ 1

Author(s):

Margaret Kimani ◽

Joost Hoedjes ◽

Zhongbo Su

Keyword(s):

Large Scale ◽

Rainfall Variability ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Rain Gauge ◽

Climate Data ◽

Error Distributions ◽

Precipitation Estimation ◽

Scatter Plots ◽

Global Precipitation

Accurate and consistent rainfall observations are vital for climatological studies in support of better planning and decision making. However, estimation of accurate spatial rainfall is limited by sparse rain gauge distributions. Satellite rainfall products can thus potentially play a role in spatial rainfall estimation but their skill and uncertainties need to be under-stood across spatial-time scales. This study aimed at assessing the temporal and spatial performance of seven satellite products (TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Center (CPC) Morphing (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP) and Global Precipitation Climatology Project (GPCP) using gridded (0.05o) rainfall data over East Africa for 15 years(1998-2012). The products’ error distributions were qualitatively compared with large scale horizontal winds (850 mb) and elevation patterns with respect to corresponding rain gauge data for each month during the ‘long’ (March-May) and ‘short’ (October-December) rainfall seasons. For validation only rainfall means extracted from 284 rain gauge stations were used, from which qualitative analysis using continuous statistics of Root Mean Squared Difference, Standard deviations, Correlations, coefficient of determinations (from scatter plots) were used to evaluate the products’ performance. Results revealed rainfall variability dependence on wind flows and modulated by topographic influences. The products’ errors showed seasonality and dependent on rainfall intensity and topography. Single sensor and coarse resolution products showed lowest performance on high ground areas. All the products showed low skills in retrieving rainfall during ‘short’ rainfall season when orographic processes were dominant. CHIRPS, CMORPH and TRMM performed well, with TRMM showing the best performance in both seasons. There is need to reduce products’ errors before applications.

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An Update on the Oceanic Precipitation Rate and Its Zonal Distribution in Light of Advanced Observations from Space

Journal of Climate ◽

10.1175/jcli-d-13-00679.1 ◽

2014 ◽

Vol 27 (11) ◽

pp. 3957-3965 ◽

Cited By ~ 69

Author(s):

Ali Behrangi ◽

Graeme Stephens ◽

Robert F. Adler ◽

George J. Huffman ◽

Bjorn Lambrigtsen ◽

...

Keyword(s):

Tropical Rainfall Measuring Mission ◽

High Sensitivity ◽

Global Precipitation Climatology Project ◽

Precipitation Rate ◽

Global Ocean ◽

Zonal Distribution ◽

Earth Observing System ◽

Precipitation Estimates ◽

Global Precipitation ◽

Oceanic Precipitation

Abstract This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua’s Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors’ estimate for 3-yr (2007–09) near-global (80°S–80°N) oceanic mean precipitation rate is ~2.94 mm day−1. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68 mm day−1) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82 mm day−1). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCP and CMAP, especially in the Southern Hemisphere.

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The Precipitation Characteristics of ISCCP Tropical Weather States

Journal of Climate ◽

10.1175/jcli-d-11-00718.1 ◽

2013 ◽

Vol 26 (3) ◽

pp. 772-788 ◽

Cited By ~ 25

Author(s):

Dongmin Lee ◽

Lazaros Oreopoulos ◽

George J. Huffman ◽

William B. Rossow ◽

In-Sik Kang

Keyword(s):

Large Scale ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Surface Precipitation ◽

Daily Data ◽

Precipitation Characteristics ◽

Dominant State ◽

Tropical Weather ◽

Tropical Hydrology ◽

Global Precipitation

Abstract The authors examine the daytime precipitation characteristics of the International Satellite Cloud Climatology Project (ISCCP) weather states in the extended tropics (35°S–35°N) for a 10-yr period. The main precipitation dataset used is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis operational product 3B42 dataset, but Global Precipitation Climatology Project daily data are also used for comparison. It is found that the most convectively active ISCCP weather state (WS1), despite an occurrence frequency below 10%, is the most dominant state with regard to surface precipitation, producing both the largest mean precipitation rates when present and the largest percent contribution to the total precipitation of the tropics; yet, even this weather state appears to not precipitate about half the time, although this may be to some extent an artifact of detection and spatiotemporal matching limitations of the precipitation dataset. WS1 exhibits a modest annual cycle of the domain-average precipitation rate, but notable seasonal shifts in its geographic distribution. The precipitation rates of the other weather states appear to be stronger when occurring before or after WS1. The precipitation rates of the various weather states are different between ocean and land, with WS1 producing higher daytime rates on average over ocean than land, likely because of the larger size and more persistent nature of oceanic WS1s. The results of this study, in addition to advancing the understanding of tropical hydrology, can serve as higher-order diagnostics for evaluating the realism of tropical precipitation distributions in large-scale models.

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Prototyping a Generic, Unified Land Surface Classification and Screening Methodology for GPM-Era Microwave Land Precipitation Retrieval Algorithms

Journal of Applied Meteorology and Climatology ◽

10.1175/2010jamc2572.1 ◽

2011 ◽

Vol 50 (6) ◽

pp. 1200-1211 ◽

Cited By ~ 5

Author(s):

Arief Sudradjat ◽

Nai-Yu Wang ◽

Kaushik Gopalan ◽

Ralph R. Ferraro

Keyword(s):

Land Surface ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Ancillary Data ◽

Surface Cover ◽

Cover Type ◽

Retrieval Algorithms ◽

Surface Classification ◽

Microwave Imager ◽

Global Precipitation

AbstractA prototype generic, unified land surface classification and screening methodology for Global Precipitation Measurement (GPM)-era microwave land precipitation retrieval algorithms by using ancillary datasets is developed. As an alternative to the current radiometer-determined approach, the new methodology is shown to be promising in improving rain detection by providing better surface-cover-type information. The early prototype new surface screening scheme was applied to the current version of the Goddard profiling algorithm that is used for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (GPROFV6). It has shown improvements in surface-cover-type classification and hence better precipitation retrieval comparisons with TRMM precipitation radar level-2 (L2) (2A25) data and the Global Precipitation Climatology Project (GPCP) version-2.1 (GPCPV2.1) datasets. The new ancillary data approach removes the current dependency of the screening step on relatively different satellite-specific channels and ensures the comparability and continuity of satellite-based precipitation products from different platforms. This is particularly important for advancing the current state of precipitation retrieval over land and for use in merged rainfall products.

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Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale

Remote Sensing ◽

10.3390/rs11232755 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2755 ◽

Cited By ~ 4

Author(s):

Mojtaba Sadeghi ◽

Ata Akbari Asanjan ◽

Mohammad Faridzad ◽

Vesta Afzali Gorooh ◽

Phu Nguyen ◽

...

Keyword(s):

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Global Scale ◽

Climate Data ◽

Promising Alternative ◽

Rain Gauges ◽

Precipitation Estimation ◽

Precipitation Records ◽

Global Precipitation ◽

Trmm 3B42

Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based precipitation estimations are a promising alternative to rain gauges for providing homogeneous precipitation information. Most satellite-based precipitation products suffer from short-term data records, which make them unsuitable for various climatological and hydrological applications. However, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) provides more than 35 years of precipitation records at 0.25° × 0.25° spatial and daily temporal resolutions. The PERSIANN-CDR algorithm uses monthly Global Precipitation Climatology Project (GPCP) data, which has been recently updated to version 2.3, for reducing the biases in the output of the PERSIANN model. In this study, we constructed PERSIANN-CDR using the newest version of GPCP (V2.3). We compared the PERSIANN-CDR dataset that is constructed using GPCP V2.3 (from here on referred to as PERSIANN-CDR V2.3) with the PERSIANN-CDR constructed using GPCP V2.2 (from here on PERSIANN-CDR V2.2), at monthly and daily scales for the period from 2009 to 2013. First, we discuss the changes between PERSIANN-CDR V2.3 and V2.2 over the land and ocean. Second, we evaluate the improvements in PERSIANN-CDR V2.3 with respect to the Climate Prediction Center (CPC) unified gauge-based analysis, a gauged-based reference, and Tropical Rainfall Measuring Mission (TRMM 3B42 V7), a commonly used satellite reference, at monthly and daily scales. The results show noticeable differences between PERSIANN-CDR V2.3 and V2.2 over oceans between 40° and 60° latitude in both the northern and southern hemispheres. Monthly and daily scale comparisons of the two bias-adjusted versions of PERSIANN-CDR with the above-mentioned references emphasize that PERSIANN-CDR V2.3 has improved mostly over the global land area, especially over the CONUS and Australia. The updated PERSIANN-CDR V2.3 data has replaced V2.2 data for the 2009–2013 period on CHRS data portal and NOAA National Centers for Environmental Information (NCEI) Program.

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Precipitation Seasonality over the Indian Subcontinent: An Evaluation of Gauge, Reanalyses, and Satellite Retrievals

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0106.1 ◽

2015 ◽

Vol 16 (2) ◽

pp. 631-651 ◽

Cited By ~ 52

Author(s):

Sapna Rana ◽

James McGregor ◽

James Renwick

Keyword(s):

Large Scale ◽

Indian Subcontinent ◽

Rainfall Variability ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Summer Monsoon Rainfall ◽

Mountainous Regions ◽

Precipitation Seasonality ◽

Satellite Retrievals ◽

Global Precipitation

Abstract This paper evaluates the seasonal (winter, premonsoon, monsoon, and postmonsoon) performance of seven precipitation products from three different sources: gridded station data, satellite-derived data, and reanalyses products over the Indian subcontinent for a period of 10 years (1997/98–2006/07). The evaluated precipitation products are the Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE), the Climate Prediction Center unified (CPC-uni), the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM) post-real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Several verification measures are employed to assess the accuracy of the data. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution, albeit with pronounced seasonal and/or regional differences. Compared to APHRODITE, the gauge-only (CPC-uni) and the satellite-derived precipitation products (GPCP, 3B42-V6, and 3B42-V7) capture the summer monsoon rainfall variability better than CFSR and ERA-Interim. Similar conclusions are drawn for the postmonsoon season, with the exception of 3B42-V7, which underestimates postmonsoon precipitation. Over mountainous regions, 3B42-V7 shows an appreciable improvement over 3B42-V6 and other gauge-based precipitation products. Significantly large biases/errors occur during the winter months, which are likely related to the uncertainty in observations that artificially inflate the existing error in reanalyses and satellite retrievals.

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Closing the Global Water Vapor Budget with AIRS Water Vapor, MERRA Reanalysis, TRMM and GPCP Precipitation, and GSSTF Surface Evaporation

Journal of Climate ◽

10.1175/2011jcli4154.1 ◽

2011 ◽

Vol 24 (24) ◽

pp. 6307-6321 ◽

Cited By ~ 26

Author(s):

Sun Wong ◽

Eric J. Fetzer ◽

Brian H. Kahn ◽

Baijun Tian ◽

Bjorn H. Lambrigtsen ◽

...

Keyword(s):

Water Vapor ◽

Large Scale ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Temporal Variations ◽

Surface Evaporation ◽

Nasa Goddard Space ◽

Intraseasonal Variations ◽

Global Precipitation ◽

Water Vapor Budget

Abstract The authors investigate if atmospheric water vapor from remote sensing retrievals obtained from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS) and the water vapor budget from the NASA Goddard Space Flight Center (GSFC) Modern Era Retrospective-analysis for Research and Applications (MERRA) are physically consistent with independently synthesized precipitation data from the Tropical Rainfall Measuring Mission (TRMM) or the Global Precipitation Climatology Project (GPCP) and evaporation data from the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF). The atmospheric total water vapor sink (Σ) is estimated from AIRS water vapor retrievals with MERRA winds (AIRS–MERRA Σ) as well as directly from the MERRA water vapor budget (MERRA–MERRA Σ). The global geographical distributions as well as the regional wavelet amplitude spectra of Σ are then compared with those of TRMM or GPCP precipitation minus GSSTF surface evaporation (TRMM–GSSTF and GPCP–GSSTF P − E, respectively). The AIRS–MERRA and MERRA–MERRA Σs reproduce the main large-scale patterns of global P − E, including the locations and variations of the ITCZ, summertime monsoons, and midlatitude storm tracks in both hemispheres. The spectra of regional temporal variations in Σ are generally consistent with those of observed P − E, including the annual and semiannual cycles, and intraseasonal variations. Both AIRS–MERRA and MERRA–MERRA Σs have smaller amplitudes for the intraseasonal variations over the tropical oceans. The MERRA P − E has spectra similar to that of MERRA–MERRA Σ in most of the regions except in tropical Africa. The averaged TRMM–GSSTF and GPCP–GSSTF P − E over the ocean are more negative compared to the AIRS–MERRA, MERRA–MERRA Σs, and MERRA P − E.

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Seasonality of African Precipitation from 1996 to 2009

Journal of Climate ◽

10.1175/jcli-d-11-00157.1 ◽

2012 ◽

Vol 25 (12) ◽

pp. 4304-4322 ◽

Cited By ~ 87

Author(s):

Brant Liebmann ◽

Ileana Bladé ◽

George N. Kiladis ◽

Leila M. V. Carvalho ◽

Gabriel B. Senay ◽

...

Keyword(s):

Annual Cycle ◽

Daily Precipitation ◽

Warning System ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Horn Of Africa ◽

Wet Season ◽

Precipitation Climatology ◽

Daily Data ◽

Global Precipitation

Abstract A precipitation climatology of Africa is documented using 12 years of satellite-derived daily data from the Global Precipitation Climatology Project (GPCP). The focus is on examining spatial variations in the annual cycle and describing characteristics of the wet season(s) using a consistent, objective, and well-tested methodology. Onset is defined as occurring when daily precipitation consistently exceeds its local annual daily average and ends when precipitation systematically drops below that value. Wet season length, rate, and total are then determined. Much of Africa is characterized by a single summer wet season, with a well-defined onset and end, during which most precipitation falls. Exceptions to the single wet season regime occur mostly near the equator, where two wet periods are usually separated by a period of relatively modest precipitation. Another particularly interesting region is the semiarid to arid eastern Horn of Africa, where there are two short wet seasons separated by nearly dry periods. Chiefly, the summer monsoon spreads poleward from near the equator in both hemispheres, although in southern Africa the wet season progresses northwestward from the southeast coast. Composites relative to onset are constructed for selected points in West Africa and in the eastern Horn of Africa. In each case, onset is often preceded by the arrival of an eastward-propagating precipitation disturbance. Comparisons are made with the satellite-based Tropical Rainfall Measuring Mission (TRMM) and gauge-based Famine Early Warning System (FEWS NET) datasets. GPCP estimates are generally higher than TRMM in the wettest parts of Africa, but the timing of the annual cycle and average onset dates are largely consistent.

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Validation of Satellite-Based Precipitation Products from TRMM to GPM

Remote Sensing ◽

10.3390/rs13091745 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1745

Author(s):

Jianxin Wang ◽

Walter A. Petersen ◽

David B. Wolff

Keyword(s):

Regional Differences ◽

Tropical Rainfall Measuring Mission ◽

Decomposition Approach ◽

Precipitation Product ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Error Decomposition ◽

Improved Accuracy ◽

Global Precipitation ◽

Statistical Metrics

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

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