Jeffrey R. McCollum

Discrepancy between Gauges and Satellite Estimates of Rainfall in Equatorial Africa

Abstract The Global Precipitation Climatology Project (GPCP) satellite estimates have approximately twice the magnitude of estimates produced from the rain gauges used by the GPCP in central equatorial Africa. Different possible explanations are identified and investigated. The first is that there may not be enough GPCP rain gauges in the area to provide accurate estimates of rainfall for comparisons with satellite estimates. A comparison of the time-averaged GPCP rain gauge estimate with a long-term (over 40 yr) climatology indicates that the GPCP gauge estimates are similar to long-term rainfall averages, suggesting that the GPCP rain gauge analysis is not underestimating rainfall. Two other possible explanations related to the physical properties of the air masses in this region are studied. Evidence from the literature and from estimates of the effective radii of cloud droplets suggests that there may be an abundance of aerosols in central Africa, resulting in an abundance of cloud condensation nuclei...

Evaluation of Biases of Satellite Rainfall Estimation Algorithms over the Continental United States

A bias-adjusted radar rainfall product is created and used for evaluation of two satellite rainfall estimation algorithms. Three years of collocated rainfall estimates from radar, rain gauges, a microwave satellite algorithm, and a multispectral (visible through near-infrared) algorithm were collected over the continental United States from July 1998 through July 2001. The radar and gauge data are compared to determine the locations and times at which the rainfall occurrences estimated by these two sensors are in sufficient agreement for the data to be used for validation. This procedure serves as quality control for both sensors and determines the locations at which the radar has difficulty detecting rainfall and should not be used in a validation dataset. For the data remaining after quality control, the gauge data are used for multiplicative adjustment of the radar estimates to remove the radar bias with respect to the gauges. These bias-adjusted estimates are compared with the satellite rainfall estimates to observe the evolution of the satellite biases over the 3-yr period. The multispectral algorithm was under development throughout the 3-yr period, and improvement is evident. The microwave algorithm overestimates rainfall in the summer months, underestimates in the winter months, and has an east-to-west bias gradient, all of which are consistent with physical explanations and previous findings. The multispectral algorithm bias depends highly on diurnal sampling; there is much greater overestimation for the daytime overpasses. These results are applicable primarily to the eastern half of the United States, because few data in the western half remain after quality control.

Initial Validation of the Global Precipitation Climatology Project Monthly Rainfall over the United States

Abstract The Global Precipitation Climatology Project (GPCP) established a multiyear global dataset of satellite-based estimates of monthly rainfall accumulations averaged over a grid of 2.5° × 2.5° geographical boxes. This paper describes an attempt to quantify the error variance of these estimates at selected reference sites. Fourteen reference sites were selected over the United States at the GPCP grid locations where high-density rain gauge network and high-quality data are available. A rigorous methodology for estimation of the error statistics of the reference sites was applied. A method of separating the reference error variance from the observed mean square difference between the reference and the GPCP products was proposed and discussed. As a result, estimates of the error variance of the GPCP products were obtained. Two kinds of GPCP products were evaluated: 1) satellite-only products, and 2) merged products that incorporate some rain gauge data that were available to the project. The error anal...

Radar Rainfall Estimation for Ground Validation Studies of the Tropical Rainfall Measuring Mission

Abstract This study presents a multicomponent rainfall estimation algorithm, based on weather radar and rain gauge network, that can be used as a ground-based reference in the satellite Tropical Rainfall Measuring Mission (TRMM). The essential steps are constructing a radar observable, its nonlinear transformation to rainfall, interpolation to rectangular grid, constructing several timescale accumulations, bias adjustment, and merging of the radar rainfall estimates and rain gauge data. Observations from a C-band radar in Darwin, Australia, and a local network of 54 rain gauges were used to calibrate and test the algorithm. A period of 25 days was selected, and the rain gauges were split into two subsamples to apply cross-validation techniques. A Z–R relationship with continuous range dependence and a temporal interpolation scheme that accounts for the advection effects is applied. An innovative methodology was used to estimate the algorithm controlling parameters. The model was globally optimized by usin...

Microwave Rainfall Estimation over Coasts

Abstract The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature ...

UNCERTAINTY OF MONTHLY RAINFALL ESTIMATES FROM RAIN GAUGES IN THE GLOBAL PRECIPITATION CLIMATOLOGY PROJECT

The Global Precipitation Climatology Project (GPCP), initiated by the World Climate Research Program, has as a main objective the production of monthly global rainfall estimates on a 2.5° × 2.5° longitude/latitude grid by combining different sources of information such as satellite remote sensing and rain gauges. It is important to understand the accuracy of the rain gauge estimates of mean area rainfall because these are considered the most reliable estimates for regions with high numbers of gauges. Methods to model the error resulting from using rain gauge data to estimate the spatially averaged rainfall accumulation have been developed previously. Three of these methods are investigated in this study: an empirical equation derived from rain gauge data, an analytical equation derived from statistical concepts, and an empirical equation derived from theory with parameters determined from calibration. Simulations are performed to determine the utility of these three approaches in estimating the error of the rain gauge mean in the context of the GPCP.

Investigations of error sources of the Global Precipitation Climatology Project emission algorithm

An error analysis of the Global Precipitation Climatology Project emission algorithm is conducted to identify the major error sources of the algorithm and to quantify the estimation biases resulting from these sources. Several error sources are investigated and uncertainties in spatial rainfall rate variability and freezing level height are identified as the most significant. Spatial rainfall rate variability contributes to beam filling error, and beam filling error is investigated in detail by simulation of brightness temperatures resulting from rainfall rate fields with known spatial variabilities. The algorithm is used to estimate rainfall from the simulated brightness temperatures, and the estimation bias is calculated by comparison of the monthly rainfall estimates with the monthly rainfall used to simulate the brightness temperatures. The final result of the study is the mean monthly multiplicative bias error as a function of the freezing level height and the instantaneous rainfall rate variability.

Rainfall over land from the AMSR-E

Significant improvements in the retrieval of instantaneous rain rates over land have occurred through the continued evolution of the Goddard Profiling Algorithm (GPROF), which has been used in the TRMM mission and most recently, the Aqua Advanced Microwave Scanning Radiometer (AMSR-E). At present, GPROF V6 incorporates a probability of convective rain, in conjunction with a convective and stratiform set of rain radiance vectors, which are then used to compute a final surface rain rate. Preliminary results with TMI and AMSR-E over the United States in the instantaneous scale suggest low bias errors and high correlations when compared with rain gauge adjusted radar rainfall estimates. Discussion on the use of specialized ground validation sites at Eureka, California and Iowa City, Iowa are also presented.

Oceanic Rainfall Estimation: Sampling Studies of the Fractional-Time-in-Rain Method

Abstract The relationship between monthly mean area-averaged rainfall and monthly mean fractional rainfall occurrence is used to develop a new method of open ocean rainfall estimation. This method uses acoustic sensors attached to drifting buoys to sample rainfall occurrence in space and time. The fractional rainfall occurrences measured by the sensors are used in a linear relationship to estimate monthly rainfall averaged over large (i.e., 2.5° × 2.5°) areas. This estimation method is tested for different scenarios using a stochastic model. Results support the feasibility of this new rainfall estimation scheme. Simulations show that the existing density of drifting buoys is inadequate, but densities around 10 times the existing density will give correlation coefficients between estimated and true rainfall around 0.55. Estimates obtained with this method may be used to calibrate and/or validate the satellite-based methods of open ocean rainfall.

On the relationship between the GOES precipitation index and ISCCP data set variables

The GOES Precipitation Index (GPI) is used for global, monthly rainfall estimation in the Global Precipitation Climatology Project (GPCP). Previous work has identified the existence of locally and seasonally varying bias in the GPI estimates. Most sources of bias involve cloud properties, as the GPI method uses the fraction of pixels with infrared cloud temperature below 235°K to estimate monthly rainfall totals averaged over 2.5° x 2.5° latitude/longitude grid boxes. In this work, the bias in the GPI is compared to cloud variables derived by the International Satellite Cloud Climatology Project (ISCCP). ISCCP data are used as predictor variables in regression models with the GPI estimation error as the dependent variable. The GPI estimation error is calculated using the global rain gage analysis produced by the GPCP for those locations where the rain gage network density is high. Fourteen ISCCP cloud variables were selected as the predictors in linear and nonlinear regression models. The nonlinear model explains over 60% of the variance of the GPI bias, while the linear model explains about 45% of the variance. Comparison with another method of GPI bias estimation is discussed.