John Schmidt, Senior Hydrometeorological Analysis Support Forecaster, Arkansas-Red Basin River Forecast Center, Tulsa, OK

Bill Lawrence, Development and Operations Hydrologist, Arkansas-Red Basin River Forecast Center, Tulsa, OK

Billy Olsen, Hydrologist-in-Charge, Arkansas-Red Basin River Forecast Center, Tulsa, OK

The Arkansas-Red Basin River Forecast Center (ABRFC) is tasked with monitoring the hydrometeorological state of the Arkansas and Red Rivers from their headwaters in the Colorado and New Mexico Rocky Mountains to Pine Bluff, AR and Fulton, AR, respectively. These 200,000 plus square miles of terrain range from the 14,000-foot peaks of the Continental Divide in Colorado through the Southern Plains toward the Mississippi River Valley. Since July 1994, the ABRFC has utilized Weather Surveillance Doppler Radar (WSR-88D) and observed precipitation amounts to create gridded precipitation estimates for its area of responsibility. The addition of WSR-88D precipitation estimates to the precipitation processing procedure gives a spatial resolution to rainfall distribution that is not available when using gage-only estimates. These processed precipitation estimates are meticulously quality-controlled and much effort is spent by the Hydrometeorological Analysis Support (HAS) forecasters at the ABRFC to create the most accurate estimate of precipitation available.

These precipitation estimates are the primary input to the Sacramento Soil Moisture Accounting Model (SAC-SMA), ABRFC’s operational hydrologic forecast model. Therefore, it is vital to determine which methodology of precipitation processing results in the most accurate and most useful estimate to ensure the accuracy of hydrologic forecasts both now and in the future. Additionally, as semi-distributed and distributed hydrologic models are developed and implemented in an operational environment, the most accurate estimates of the spatial distribution and amount of rainfall are vital to optimizing their performance.

Currently, there are two methods of estimating precipitation spatially in an operational environment. The first, and older, is the use of observed precipitation measurements. These measurements are accepted as the most accurate. However, they are a point measurement and must be distributed spatially to be used in hydrologic forecasting. The distribution is accomplished using some sort of weighting technique, either Thiessen polygon, predetermined weights or isohyetal analysis. The second method is the remote sensing of precipitation amounts using the WSR-88D. This method gives a spatial distribution of precipitation that cannot be matched by a gage network, however the amounts of rainfall are often inaccurate. The goal of the HAS function at the ABRFC is to optimize the benefits of each methodology to arrive at the most accurate estimation of precipitation possible.

At this time, there are at least three methods of precipitation processing used operationally in the National Weather Service (NWS) River Forecast Centers (RFCs). Gage-only mean areal precipitation (MAP) estimates are used by some RFCs as their operational precipitation input to their hydrologic model. Two other methods strive to integrate WSR-88D estimates of precipitation and observed site-specific measurements of precipitation. Stage III was developed in the late 1980s by the NWS Office of Hydrology (OH). It computes an average bias across a radar’s umbrella by comparing some observed gage reports to the corresponding WSR-88D precipitation estimate. The average bias of these sites is then applied to every grid cell within that radar’s coverage. Individual radars are then mosaicked together. Areas that are covered by more than one radar are resolved by either accepting the largest value or the average value of all multisensor precipitation estimates, depending on the user’s preference. This is a rather simplistic overview of Stage III, but it captures the key assumption that the ABRFC believes accounts for the underestimation of rainfall by Stage III, the use of a single average bias per radar.

In 1996, ABRFC developed and implemented a different precipitation-processing algorithm that places more emphasis on the observed gage network, Process 1 (P1). P1’s roots are in the program RAIN, developed by Brian McCormick at the US Army Corps of Engineers, Tulsa District. First, each radar’s hourly precipitation estimate is mosaicked together by using the average value where coverages overlap. P1 then calculates a unique bias for each HRAP grid cell, rather than Stage III’s single bias per radar. This is done by using a double interpolation technique. If an observed rainfall amount is available for a grid cell, that grid cell’s precipitation estimate is set to that value and the difference between the WSR-88D rainfall estimate and the observation is calculated. Along a line between two such grid cells an adjusted radar estimate is calculated using a weighted interpolation scheme. This weighting is determined based on how far the grid cell is from each of the two endpoints of the line. This results in a unique bias for each cell. For grid cells that lie between these "bias lines" a bias is calculated by interpolating from the surrounding "bias lines."

There have been several recent studies on this topic, each with its own methodology and study area. Both Stellman (et al.) and Wang (et al.) have shown that Stage III-derived mean areal precipitation (MAPX) estimates have a significant negative bias when compared to traditional gage-only MAP estimates for many individual basins in the Southeast River Forecast Center, Lower Mississippi Forecast Center and ABRFC region. However, it is important to note that NWS MAPX estimates can be subdivided into two groups, Stage III-derived and P1-derived.

This study compared spatially distributed National Climatic Data Center (NCDC) observations of precipitation to the ABRFC P1-derived values on a monthly, grid-by-grid basis from January 1997 to September 1999. NCDC gage data were used because they are delivered in a tabular format and monthly duration. The gage-only fields were created using 1000+ gages in and around the ABRFC with 544 of these gages falling within the ABRFC boundary (Fig. 1). These numbers are comparable with the number of gages used operationally at the ABRFC. Over the past three years, the number of daily precipitation observations inside the ABRFC have ranged from 27 to 686 with an average of 412 on any given day. These monthly gage values were distributed spatially using a second-power inverse distance-weighting (IDW) scheme available in ArcView. Figure 2 provides an example of the May 1998 gage-only IDW precipitation distribution.

Figure 1: Distribution of NCDC monthly cooperative stations across the ABRFC region.

Monthly P1 fields were reprojected from the Hydrologic Rainfall Analysis Project (HRAP) projection, in which they are created, into a geographic projection using an ArcView Avenue extension provided by the NWS Hydrologic Research Laboratory (HRL). Figure 3 represents a 1-month accumulation of P1 precipitation estimates for May 1998. Both of these fields were then exported as delimited text files from ArcView in HRAP projection. Two simple C program language routines were then run on these delimited files to mask out only those HRAP grid bins that lie within the ABRFC boundary and then sum those values. The final product was a monthly volume of rainfall across the entire ABRFC basin for both gage-only and P1 precipitation estimates.