Mark L. Morrissey

Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting

Abstract Next-Generation Weather Radar (NEXRAD) multisensor precipitation estimates will be used for a host of applications that include operational streamflow forecasting at the National Weather Service River Forecast Centers (RFCs) and nonoperational purposes such as studies of weather, climate, and hydrology. Given these expanding applications, it is important to understand the quality and error characteristics of NEXRAD multisensor products. In this paper, the issues involved in evaluating these products are examined through an assessment of a 5.5-yr record of multisensor estimates from the Arkansas–Red Basin RFC. The objectives were to examine how known radar biases manifest themselves in the multisensor product and to quantify precipitation estimation errors. Analyses included comparisons of multisensor estimates based on different processing algorithms, comparisons with gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet, and the application of a validation f...

An Examination of the Diurnal Cycle in Oceanic Tropical Rainfall Using Satellite and In Situ Data

Abstract Diurnal variations in tropical cold cloudiness are examined for the period 1986–90 for each 2.5° latitude–longitude area in the global Tropics. The fractional coverage of cold cloudiness, as determined from various IR brightness temperature thresholds, has been used as a proxy for tropical convective precipitation, as direct observations of rainfall are unavailable for much of the earth, especially over the oceans. Variations in fractional coverage of cold cloud for three different temperature thresholds are examined: 235.225, and 215 K. The results of this study indicate that cold cloud is most frequently observed over land between 1800 and 2100 local time and is independent of the temperature threshold used. Over the tropical oceans, however, the time of maximum occurrence of cold cloud varies substantially with the temperature threshold employed. Coldest cloud-top temperatures (< 215 K) are found to occur much earlier in the day than warmer cloud tops and peak between 0300 and 0600 local time,...

Conditional Bias in Radar Rainfall Estimation

Abstract The goal of this study is to improve understanding of the optimization criteria for radar rainfall (RR) products. Conditional bias (CB) is formally defined and discussed. The CB is defined as the difference between a given rain rate and the conditional average of its estimates. A simple analytical model is used to study the behavior of CB and its effect on the relationship between the estimates and the truth. This study shows the measurement errors of near-surface radar reflectivity and the natural reflectivity–rainfall rate variability can affect CB. This RR estimation error component is also compared with the commonly used mean-square error (MSE). A dilemma between the minimization of these two errors is demonstrated. Removing CB from the estimates significantly increases MSE, but minimizing MSE results in a large CB that manifests itself in underestimation of strong rainfalls.

Error Uncertainty Analysis of GPCP Monthly Rainfall Products: A Data-Based Simulation Study

Abstract This paper focuses on estimating the error uncertainty of the monthly 2.5° × 2.5° rainfall products of the Global Precipitation Climatology Project (GPCP) using rain gauge observations. Two kinds of GPCP products are evaluated: the satellite-only (MS) product, and the satellite–gauge (SG) merged product. The error variance separation (EVS) method has been proposed previously as a means of estimating the error uncertainty of the GPCP products. In this paper, the accuracy of the EVS results is examined for a variety of gauge densities. Three validation sites—two in North Dakota and one in Thailand—all with a large number of rain gauges, were selected. The very high density of the selected sites justifies the assumption that the errors are negligible if all gauges are used. Monte Carlo simulation studies were performed to evaluate sampling uncertainty for selected rain gauge network densities. Results are presented in terms of EVS error uncertainty normalized by the true error uncertainty. These res...

A Detailed Evaluation of GPCP 1° Daily Rainfall Estimates over the Mississippi River Basin

Abstract This study provides an intensive evaluation of the Global Precipitation Climatology Project (GPCP) 1° daily (1DD) rainfall products over the Mississippi River basin, which covers 435 1° latitude × 1° longitude grids for the period of January 1997–December 2000 using radar-based precipitation estimates. The authors’ evaluation criteria include unconditional continuous, conditional (quasi) continuous, and categorical statistics, and their analyses cover annual and seasonal time periods. The authors present spatial maps that reflect the results for the 1° grids and a summary of the results for three selected regions. They also develop a statistical framework that partitions the GPCP–radar difference statistics into GPCP error and radar error statistics. They further partition the GPCP error statistics into sampling error and retrieval error statistics and estimate the sampling error statistics using a data-based resampling experiment. Highlights of the results include the following: 1) the GPCP 1DD ...

An Examination of the East Pacific ITCZ Rainfall Distribution

Abstract Very few (if any) in situ measurements of rainfall are available in the Pacific ITCZ east of the Line Islands (157°W). Hence, climatological datasets, which are assembled from various in situ sources, and satellite-derived analyses of precipitation are frequently relied upon to provide information on the distribution of rainfall in this important region. A substantial amount of disagreement exists among these information sources as demonstrated in this paper. In particular, the east–west gradient of estimated rainfall intensity in the eastern Pacific ITCZ is quite different during the Northern Hemisphere warm season among six different satellite algorithms (one infrared and five microwave) and two climatologies that are examined. Some of these data suggest that a local minimum in rainfall intensity is located near 140°W in the Pacific ITCZ during northern summer, with increasing intensity toward the east and west, while the others depict steadily decreasing rainfall intensity from west of the Ame...

Using sparse raingages to test satellite‐based rainfall algorithms

A statistical method is presented which uses sparsely distributed raingages to obtain calibrations for satellite-based rainfall algorithms. The method can also be used for the development and verification of these algorithms. It is assumed that the distributions of areal rainfall, given a value of a satellite areal rainfall estimate, are homogeneous and stationary over a region encompassing a network of sparse raingages. The method may best be applied over open ocean regions to supplement the limited number of dense raingage/radar networks there. A description of the method is given, followed by an illustration of the method using simulated rain fields. Finally, the method is used to calibrate a simple satellite rainfall algorithm using Pacific atoll monthly raingage data.

Sampling-Induced Conditional Biases in Satellite Climate-Scale Rainfall Estimates

Abstract The effect of temporal sampling error in satellite estimates of climate-scale rainfall is to produce a “conditional” bias where the algorithm overestimates high rainfall and underestimates low rainfall. Thus, the bias is conditional on the value of the estimate. This paper illustrates the problem using satellite infrared rainfall estimates together with a well-known satellite algorithm and shows it to be a function of the averaging scale, the sampling rate, and the temporal autocorrelation structure of the satellite estimates. Using realistic sampling rates, it is shown that significant biases exist in satellite rainfall estimates if polar-orbiting data are used in their construction. A simple correction for this bias based upon the estimated autocorrelation structure is given.

An investigation of the sources of summertime haze in the Blue Ridge Mountains using multivariate statistical methods

Abstract Multivariate statistical analyses are employed to identify the source areas of the fine particulates and sulfate, which are the primary components of summer haze in the Blue Ridge Mountains of Virginia. These analyses include principal component analysis followed by stepwise multiple regression analysis. The results indicate that most of the fine particles and sulfates originate in the Midwest. The most important factor for both parameters is the residence time of the air parcels over the Midwest. The results also indicate that the sulfate is formed by photochemically initiated reactions. Production of organic aerosols from natural hydrocarbon emissions is also identified as a minor source of fine particles in the Blue Ridge Mountains area.

Estimating Rainfall in the Tropics Using the Fractional Time Raining

Abstract The relationship between the fractional time raining and tropical rainfall amount is investigated using raingage data and a point process model of tropical rainfall. Both the strength and the nature of the relationship are dependent upon the resolution of the data used to estimate the fractional time raining. It is found that highly accurate estimates of rainfall amounts over periods of one month or greater can be obtained from the fractional time raining so long as high-time-resolution data are used. It is demonstrated that the relationship between the fractional time raining and monthly atoll rainfall is quasi-homogeneous within the monsoon trough region of the equatorial western Pacific.