A Real-Time System to Estimate Weather Conditions at High Resolution
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12.1 A Real-Time System to Estimate Weather Conditions at High Resolution Peter P. Neilley1 Weather Services International, Inc. Andover, MA 01810 And Bruce L. Rose The Weather Channel Atlanta, GA the earth’s surface (the so-called current 1. Introduction1 conditions). b) We do not necessarily produce weather The purpose of this paper is to describe an observations on a regular grid, but at an operational system used to estimate current irregular set of arbitrary locations or points weather conditions at arbitrary places in real- that are relevant to the consumers of the time. The system, known as High Resolution information. Assimilation of Data (or HiRAD), is designed to generate synthetic weather observations in a c) In addition to producing quantitative manner equivalent in scope, timeliness and observational elements (e.g. temperature, quality to a arbitrarily dense physical observing pressure and wind speed) our system network. Our approach is, first, to collect produces common, descriptive terminology information from a variety of relevant sources of the sensible weather such as including gridded analyses, traditional surface “Thundershowers”, “Patchy Fog”, and weather reports, radar, satellite and lightning “Snow Flurries”. observations. Then we continuously synthesize these data into weather condition estimates at d) We do not strive to produce a state of the prescribed locations. An operational system atmosphere optimized for fidelity with based on this approach has been built and is Numerical Weather Prediction (NWP) commercially deployed in the United States. models. Instead, the system is optimized to produce the most accurate estimate of the In most regards, our approach is analogous to observed state at the surface that can be modern data assimilation techniques. However, used directly as consumer weather since our objective is to produce consumer- information. oriented surface weather information, our requirements differ from that of standard Section 2 describes the methodology used to assimilation systems (e.g. objective analysis, estimate the weather conditions. Section 3 variational assimilation, Ensemble Kalman filter) provides some examples. Section 4 reports on in the following ways: analyses to ascertain the quality of the observations. Section 5 is a summary and a) We do not create a full 3-dimensional comments on our future work efforts. estimate of the state of the atmosphere; we Complementary papers (Rose et al, 2006; Koval instead focus on the observable weather at et al., 2006) on HiRAD are found elsewhere in these conference proceedings. 1 Corresponding author and presenter. Peter P. Neilley, Weather Services International, 400 Minuteman Rd, Andover, MA 01810, USA; e-mail: [email protected] 2. Methodology the 1- and 2-hr forecasts. The basic methodology employed in HiRAD is Table 1 lists the quantitative observation that of a layered approach in which a sequence variables that are extracted from the RUC. of refinements to a first-guess is made using a These variables are used in subsequent set of relevant data sources. processing by HiRAD and are the basis for deriving the final output of HiRAD 2.1 First-guess values. The initial values used by HiRAD are extracted or derived from the 2.1 Spatial Downscaling. The RUC first-guess RUC 13 km2 0-3 hr forecast data. These values are spatially refined using statistical data are collected hourly from the U.S. downscaling based on high-resolution National Weather Service (NWS) ftp servers terrain and climatological information. This and are typically available about :45 minutes downscaling partly accounts for variance in past each hour. The RUC output grids from the weather due to topographical gradients the hours surrounding the valid time of the not captured in the RUC grids. This synthesized observation are linearly downscaling is most relevant in rapidly interpolated in time. In most circumstances, varying terrain such as the Intermountain this interpolation is done between either the Western United States. The high-resolution 0-hr (analysis) and 1-hr forecast, or between climatological database used is similar to that described by Daly (1994). Table 1: Quantitative Variables Estimated by 2.2 Correction using recent observations. The HiRAD next step in the process involves correcting the downscaled first-guess values based on Temperature recent, nearby surface observations. For each recent observation, a difference Dew Point Temperature between the refined first-guess values and the actual observation is computed. These Wind Speed differences are spatially interpolated using a Kriging technique (c.f. Oliver and Webster, Cloud Cover (Percent of Sky) 1990) applied at the locations of the synthetic observations. The interpolated Probability of Precipitation Occurring correction values are then added as adjustments to the refined first-guess values Probability of Thunder Heard at this target point. Observational covariances needed for the Kriging Conditional Probability of Rain if Precipitation is interpolation are assumed to vary inversely Occurring proportional to the distance between locations. In future versions of the system Conditional Probability of Snow if Precipitation more realistic covariance matrices may be is Occurring used. The sites included in the interpolation are determined using an iterative process Conditional Probability of Ice, Freezing Rain or that selects additional observation sites that Mixed Precipitation if Precipitation is Occurring exhibit maximum covariance with the interpolation site (most relevant) but Probability of Fog minimum covariance with already selected sites (most unique). Fig. 1 illustrates an Precipitation Rate example of the site selection process. Visibility 2 The data used is based on the RUC 13-km resolution data. However, as of this writing, only the degraded 20-km version of the output grids is published by the NWS. Fig 1. Example of the site selection process outcome in the Observation Engine. The example shows the sites considered for interpolation (all dots) and the sights actually chosen (black dotes with Kriging weights in the middle). Other sites not choose are color coded for the reason they were not included. The Kriging technique assures that even if that observation is collocated with an synthesized observations created at points assimilation data point. collocated with actual observations will be equivalent to the actual observation (at least The current version of HiRAD uses only through this phase in the process). surface observations in METAR form for this Achieving this was considered an important error correction step. The METAR characteristic of HiRAD. We note that this is observations are subject to quality control typically not the case with most data (QC) before being used. This QC is based assimilation systems as those systems on differences between the observations, consider the error statistics of the the downscaled RUC first-guess values and observation, the error statistics of the statistics derived from histories of these background field and the meteorological differences. Observations that fall outside consistency of the observation when configurable ranges of variance from the incorporating the observation. Hence, unlike downscaled first-guess are rejected. HiRAD, most data assimilation techniques generally do not replicate an observation Symrna GA 22-Jun 2004 1900 UTC Fig 2. Illustration of the fingerprinting and calibration processes of the Observation Engine for a case near Smyrna GA on 22 June 2004, 1900 UTC. The images on the right show the time series of radar data leading up to the observation time. For each neighboring site near Smyrna, the columns on the left indicate METAR weather observations, local radar snapshots, fingerprinter- estimated weather and the “distance” between the two (see text). Before inclusion in HiRAD, the METAR 2.3 Lighting Data. Current lightning ground cloud reports are augmented with recent strike data (and a proportion of the cloud- mid- and upper-level cloudiness estimates cloud and in-cloud) from the United States derived from GOES satellite observations. Precision Lightning Network (USPLN) These satellite enhancements correct for the operated by Weather Decision Technologies deficiency in fully-automated ASOS-based of Norman, Oklahoma (WDT) is then METAR observations in the United States incorporated in the system. For each that are unable to detect cloud amounts or observation site, a probability of thunder is ceilings above 12,000 feet AGL. This cloud computed based on the number of nearby, augmentation step is the only direct use of recent lightning strikes recorded. Lightning satellite data in the current version of strikes within 30 nautical miles of a HiRAD. However, satellite data is used synthesized observation location are extensively in the initial RUC-based grids. considered. 2.4 Radar Data Overlay. The inclusion of fingerprinting, calibration and integration. current weather radar data in the observation estimation process is one of the 2.5.1: Fingerprinting: The purpose of the most important and complex components of radar fingerprinter is to estimate the general HiRAD. Together, radar and lightning data weather conditions in the vicinity of the generally represent the most contemporary observation site based on the character of data used by HiRAD and hence often the nearby radar data and ancillary contribute significantly to the observation information from the first-guess quantities. estimation process. In the present