Global Precipitation Climatology Project Data Research Articles

A multi-sensor climatological view of double ITCZs over the Indian Ocean

We characterize the climatological features of the double inter-tropical convergence zones (DITCZs) over the western Indian Ocean during November–December by a synergistic analysis of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS III) data (1988–2005) and the National Aeronautics and Space Administration's (NASA's) A-Train data (2002–2009). We investigate rainfall, freshwater flux and cloud liquid water, cloud fraction and relative humidity over the DITCZs. In addition, the daily rainfall data from the Global Precipitation Climatology Project (GPCP) are used to document the DITCZs during the El Niño southern oscillation (ENSO) events. An analysis of the GPCP data shows that the DITCZs are clearly discernible during strong ENSO events (1997, 2002 and 2006), in sharp contrast to the DITCZs in the eastern Pacific Ocean, where they are absent during ENSOs. Further, these convergence zones on either side of the equator are of short duration, approximately 3–6 pentads during November and December. All satellite sensor data sets consistently capture the major features of DITCZs. As an accurate simulation of DITCZs in coupled global climate models remains a challenge, the results from the present study would provide a platform for evaluating these models.

Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data

In the present study, an attempt has been made to estimate and validate the daily and monthly rainfall during the Indian summer monsoon seasons of 2008 and 2009 using INSAT (Indian National Satellite System) Multispectral Rainfall Algorithm (IMSRA) technique utilizing Kalpana-1 very high resolution radiometer (VHRR) measurements. In contrary to infrared (IR), microwave (MW) rain rates are based on measurements that sense precipitation in clouds and do not rely merely on cloud top temperature. Geostationary satellites provide broad coverage and frequent refresh measurements but microwave measurements are accurate but sparse. IMSRA technique is the combination of the infrared and microwave measurements which make use of the best features of both IR- and MW-based rainfall estimates. The development of this algorithm included two major steps: (a) classification of rain-bearing clouds using proper cloud classification scheme utilizing Kalpana-1 IR and water vapor (WV) brightness temperatures (Tb) and (b) collocation of Kalpana-1 IR brightness temperature with Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) surface rain rate and establishment of a regression relation between them. In this paper, the capability of IMSRA as an operational algorithm has been tested for the two monsoon seasons 2008 and 2009. For this, IMSRA has been used to estimate daily and monthly rainfall and has been intercompared on daily and monthly scales with TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V6 product and Global Precipitation Climatology Project (GPCP) rain product during these two monsoon years. The daily and monthly IMSRA rainfall has also been validated against ground-based observations from Automatic Weather Station (AWS) Rain Gauge and Buoy data. The algorithm proved to be in good correlation with AWS data over land up to 0.70 for daily rain estimates except orographic regions like North-East and South-West India and 0.72 for monthly rain estimates. The validation with Buoys gives the reasonable correlation of 0.49 for daily rain estimates and 0.66 for monthly rain estimates over Tropical Indian Ocean.

Analysis of trends in the Sahelian ‘rain-use efficiency’ using GIMMS NDVI, RFE and GPCP rainfall data

Rain-use efficiency (RUE; the ratio of vegetation productivity to annual precipitation) has been suggested as a measure for assessing land degradation in arid/semi-arid areas. In the absence of anthropogenic influence, RUE has been reported to be constant over time, and any observed change may therefore be attributed to non-rainfall impacts. This study presents an analysis of the decadal time-scale changes in the relationship between a proxy for vegetation productivity (ΣNDVI) and annual rainfall in the Sahel-Sudanian zone of Africa. The aim is to test the quality of data input and the usefulness of both the RUE approach and an alternative method for separating the effects on vegetation productivity of rainfall change and human impact. The analyses are based on earth observation of both rainfall (GPCP (Global Precipitation Climatology Project), 1982–2007 and RFE (Rainfall Estimate) (1996–2007)) and ΣNDVI (AVHRR GIMMS NDVI dataset, 1982–2007). It is shown that the increase in ΣNDVI has been substantial in the Sahel-Sudanian zone over the 1982–2007 period, whereas for the period 1996–2007 the pattern of ΣNDVI trends is more complex. Also, trend analysis of annual rainfall from GPCP data (2.5° resolution) and RFE data (0.1° resolution) suggests that rainfall has increased over both periods. Further it is shown that RUE values are highly correlated to rainfall, undermining the use of earth observation (EO)-based RUE (using ΣNDVI) as a means of separating rainfall impacts from other factors. An alternative method identify temporal trends in residuals of ΣNDVI, after regressing it against annual rainfall, is tested, yet is shown to be useful only where a high correlation between ΣNDVI and annual rainfall exists. For the areas in the Sahel-Sudanian zone for which this condition is fulfilled, trend analyses suggest very limited anthropogenic land degradation in the Sahel-Sudanian zone.

Low‐frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets

Evidence is presented of a reduction in relative humidity over low‐latitude and midlatitude land areas over a period of about 10 years leading up to 2008, based on monthly anomalies in surface air temperature and humidity from comprehensive European Centre for Medium‐Range Weather Forecasts reanalyses (ERA‐40 and ERA‐Interim) and from Climatic Research Unit and Hadley Centre analyses of monthly station temperature data (CRUTEM3) and synoptic humidity observations (HadCRUH). The data sets agree well for both temperature and humidity variations for periods and places of overlap, although the average warming over land is larger for the fully sampled ERA data than for the spatially and temporally incomplete CRUTEM3 data. Near‐surface specific humidity varies similarly over land and sea, suggesting that the recent reduction in relative humidity over land may be due to limited moisture supply from the oceans, where evaporation has been limited by sea surface temperatures that have not risen in concert with temperatures over land. Continental precipitation from the reanalyses is compared with a new gauge‐based Global Precipitation Climatology Centre (GPCC) data set, with the combined gauge and satellite products of the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC), Merged Analysis of Precipitation (CMAP), and with CPC's independent gauge analysis of precipitation over land (PREC/L). The reanalyses agree best with the new GPCC and latest GPCP data sets, with ERA‐Interim significantly better than ERA‐40 at capturing monthly variability. Shifts over time in the differences among the precipitation data sets make it difficult to assess their longer‐term variations and any link with longer‐term variations in humidity.

Tropical rainfall‐surface temperature relations using Tropical Rainfall Measuring Mission precipitation data

In this study, 9 years (1998–2006) of monthly precipitation data from Tropical Rainfall Measuring Mission (TRMM) are used to examine the relations between tropical rainfall and surface temperature using measurements from both passive and active microwave sensors. These relations are compared to those derived from Global Precipitation Climatology Project (GPCP) analyses. A technique is first developed to adjust the TRMM Precipitation Radar (PR) monthly rainfall data in the tropics (whole ocean and whole land) to account for the effect of the TRMM orbit boost from 350 to 402 km in August 2001. The postboost PR rainfall is adjusted by adding 6.5, 6.0, and 1.0% to the monthly PR rainfall data over the ocean at the estimated surface, the near surface, and the 2 km level, respectively. No adjustment is made for data over land or above the 4 km level. The relationships between the tropical rainfall and surface temperature are then examined with both the TRMM Microwave Imager (TMI) and adjusted PR data. Comparing tropical (25°N–25°S) ocean precipitation to mean sea surface temperature (SST) over the same area, the GPCP and TMI rainfall data have large and similar slopes (∼15%/°C) against ocean‐wide SST anomalies, while the surface monthly rainfall anomalies derived from the TRMM PR exhibit much shallower slopes (∼4%/°C) against the SST anomalies. At the 4 km level the PR data exhibit a larger slope (12%/°C) comparable to the passive microwave value. Over the tropical land, all rainfall data except TRMM PR at 6 km have similar, but negative, slopes against surface temperature anomalies, in contrast to the positive slopes over the ocean. Over the total tropics (ocean and land combined), TRMM TMI and GPCP rainfall data have rather similar smaller positive slopes (6%/°C), when compared to ocean plus land surface temperature, but the PR rainfall data slopes are slightly negative, except at the 4 km level (4%/°C). Overall, the PR‐based surface precipitation‐temperature slopes do not confirm slopes based on passive microwave observations. This may be the result of PR retrieval error or inherent passive/active retrieval differences. Further research is needed to advance the use of TRMM data in this regard.

Comparison of Four Cloud Schemes in Simulating the Seasonal Mean Field Forced by the Observed Sea Surface Temperature

Abstract The impacts of four stratiform cloud parameterizations on seasonal mean fields are investigated using the global version of the Experimental Climate Prediction Center (ECPC) global-to-regional forecast system (G-RSM). The simulated fields are compared with the International Satellite Cloud Climatology Project (ISCCP) data for clouds, the Global Precipitation Climatology Project data for precipitation, the Earth Radiation Budget Experiment and the Surface Radiation Budget data for radiation, and the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R-2) for temperature. Compared to observations, no stratiform cloud parameterization performed better in simulating all aspects of clouds, temperature, precipitation, and radiation fluxes. There are strong interactions between parameterized stratiform clouds and boundary layer clouds and convection, resulting in changes in low-level cloudiness and precipitation in the simulations. When the simulations are compared with ISCCP cloudiness and cloud water, and the NCEP/DOE R-2 relative humidity, the cloud amounts simulated by all four cloud schemes depend mostly on relative humidity with less dependency on the model’s cloud water, while the observed cloud amount is more strongly dependent on cloud water than relative humidity, suggesting that cloud parameterizations and the simulation of cloud water require further improvement.

Spatial and Temporal Variation of Precipitation in Greece and Surrounding Regions Based on Global Precipitation Climatology Project Data

Abstract In this study, the spatial and temporal distribution of precipitation in the broader Greek area is investigated for the 26-yr period 1979–2004 by using monthly mean satellite-based data, with complete spatial coverage, taken from the Global Precipitation Climatology Project (GPCP). The results show that there exists a clear contrast between the more rainy western Greek area (rainside) and the drier eastern one (rainshadow), whereas there is little precipitation over the islands, particularly in the southern parts. The computed long-term areal mean annual precipitation amount averaged for the study area is equal to P = 639.8 ± 44.8 mm yr−1, showing a decreasing trend of −2.32 mm yr−1 or −60.3 mm over the 26-yr study period, which corresponds to −9.4%. This decrease of precipitation, arising primarily in winter and secondarily in spring, is the result of a decreasing trend from 1979 through the 1980s, against an increase during the 1990s through the early 2000s, followed again by a decrease up to the year 2004. The performed analysis reveals an increasing trend of precipitation in the central and northern parts of the study region, contrary to an identified decreasing trend in the southern parts, which is indicative of threatening desertification processes in those areas in the context of climatic changes in the climatically sensitive Mediterranean basin. In addition, the analysis shows that the precipitation decrease is due to a corresponding decrease of maximum precipitation against rather unchanged minimum precipitation amounts. The analysis indicates that the changing precipitation patterns in the region during winter are significantly anticorrelated with the North Atlantic Oscillation (NAO) index values, against a positive correlation during summer, highlighting thus the role of large-scale circulation patterns for regional climates. The GPCP precipitation data are satisfactorily correlated with instrumental measurements from 36 stations uniformly distributed over the study area (correlation coefficient R = 0.74 for all stations; R = 0.63–0.91 for individual stations).

Sudan‐Sahel rainfall onset: Definition of an objective index, types of years, and experimental hindcasts

Using rainfall estimates from the 5‐day version of CPC Merged Analysis of Precipitation (CMAP) and Global Precipitation Climatology Project data along with multiscale spatial key descriptors of atmospheric dynamics from National Center for Environmental Prediction/Department of Energy reanalysis 2 (NCEP/DOE), we first define a West African monsoon onset index to determine its successive dates of occurrence over the period 1979–2004 (28–29 June in mean with a standard deviation of 8.5 days). Then we focus on the three main types of time evolutions of that index at the moment of the monsoon onset in terms of precipitation, pressure, temperature, and winds at different levels to detect the most robust associated signals and select on the mid‐May, mid‐June period different sets of potential predictors for onset dates. Basically, late (early) onsets are preceded by more (less) rainfall southward to the equator by the end of May, then by a clear decrease (increase) of the normal northward cross‐equatorial gradient in mid‐June. Finally, we present experimental cross‐validated hindcasts of the dates of onset always based on four predictors using both reanalyzed atmospheric data and observed rainfall estimates. Accurate forecasting schemes are obtained, especially with CMAP rainfall (time tendencies and cross‐equatorial gradients) over the Gulf of Guinea. In this context the hindcasted series are highly significant when compared to observations (75% of explained common variance with the sole rainfall predictors) and can reach 82% of variance when a zonal wind descriptor of regional scale is added. Basically, a late (early) onset is preceded by more (less) rainfall southward to the equator by the end of May, then by a clear decrease (increase) of the normal northward cross‐equatorial gradient by mid‐June.

Seasonal variation in precipitation patterns to the global ocean: An analysis of the GPCP version 2 data set

Precipitation affects the thermohaline circulation, chemical mass balance, and primary productivity of surface ocean waters. An analysis of temporal and spatial variation of oceanic precipitation was conducted on the Global Precipitation Climatology Project (GPCP) version 2 data set, a monthly 2.5° × 2.5° latitude‐longitude gridded data set for the period 1979 to 1999. While the precipitation patterns observed are generally similar to those reported in previous climatologies, new features and greater detail of global precipitation were revealed from our analysis of the GPCP data set. High precipitation was observed in the intertropical convergence zone (ITCZ), the South Pacific convergence zone (SPCZ), and the storm tracks in the North Pacific and Atlantic Oceans. Low precipitation was observed in the Polar regions and in the subtropics of the East Pacific, East Atlantic, and the Southeast and Northwest Indian Ocean. The spatial coverage of these high and low precipitation regions changed through the year. A strong seasonal cycle of precipitation was observed for the Northern and the Southern Hemispheres. The area‐weighted mean precipitation increased from a minimum of ∼2 mm d−1 in February to a maximum of ∼5 mm d−1 in August in the Northern Hemisphere and from a minimum of ∼2.5 mm d−1 in September to a maximum of ∼3.5 mm d−1 in March in the Southern Hemisphere. Similar seasonal cycles of precipitation were observed for each ocean basin. Global precipitation also varied significantly with both latitude and longitude, with a latitudinal maximum at 56°S, 39°S, 4°S, 6°N, 39°N, and 56°N, and a longitudinal maximum over each ocean. The seasonal varying precipitation patterns are a foundation for evaluating the effect of wet deposition on ocean circulation, flux of chemical species, and its effect on marine ecosystems.

Classification-Based Rainfall Estimation Using Satellite Data and Numerical Forecast Model Fields

Using Global Precipitation Climatology Project data gathered during June, July, and August 1989 over Japan, rainfall estimates are examined from both geostationary satellite imagery using a multifeature classification approach, and from short-term weather prediction model fields. Additionally, the utility of combining model forecast information within such a classifier to improve the final estimate is investigated. During both months satellite estimates are superior to model forecasts in detecting heavy rain events associated with extremely cold cloud tops, and in identifying cloud-free regions. Model estimates are superior to satellite retrievals in terms of dynamic range and regional bias. Addition of visible data to an infrared-only scheme improved monthly rainfall estimates during June, and hourly estimates during both months. In June it is shown that a combined satellite-model method clearly yields improved retrievals of rainfall relative to those obtained by using either satellite data or model forecasts alone at both monthly and hourly time scales. However, in July and August, satellite retrievals largely underestimated monthly rainfall, and the model produced poor hourly forecasts. Generally, a good model forecast of rainfall can enhance the satellite estimate, while a poor forecast will degrade it. If results obtained during June are found to be valid for other regions of the globe, such a method could be used to develop rainfall climatologies. It could also be used in a real-time operational numerical weather prediction environment since it is computationally rapid, with only geostationary satellite observations and model-predicted fields needed to derive the estimates.