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David T. Myrick
and
John D. Horel

Abstract

Federal, state, and other wildland resource management agencies contribute to the collection of weather observations from over 1000 Remote Automated Weather Stations (RAWS) in the western United States. The impact of RAWS observations on surface objective analyses during the 2003/04 winter season was assessed using the Advanced Regional Prediction System (ARPS) Data Assimilation System (ADAS). A set of control analyses was created each day at 0000 and 1200 UTC using the Rapid Update Cycle (RUC) analyses as the background fields and assimilating approximately 3000 surface observations from MesoWest. Another set of analyses was generated by withholding all of the RAWS observations available at each time while 10 additional sets of analyses were created by randomly withholding comparable numbers of observations obtained from all sources.

Random withholding of observations from the analyses provides a baseline estimate of the analysis quality. Relative to this baseline, removing the RAWS observations degrades temperature (wind speed) analyses by an additional 0.5°C (0.9 m s−1) when evaluated in terms of rmse over the entire season. RAWS temperature observations adjust the RUC background the most during the early morning hours and during winter season cold pool events in the western United States while wind speed observations have a greater impact during active weather periods. The average analysis area influenced by at least 1.0°C (2.5°C) by withholding each RAWS observation is on the order of 600 km2 (100 km2). The spatial influence of randomly withheld observations is much less.

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David T. Myrick
and
John D. Horel

Abstract

Experimental gridded forecasts of surface temperature issued by National Weather Service offices in the western United States during the 2003/04 winter season (18 November 2003–29 February 2004) are evaluated relative to surface observations and gridded analyses. The 5-km horizontal resolution gridded forecasts issued at 0000 UTC for forecast lead times at 12-h intervals from 12 to 168 h were obtained from the National Digital Forecast Database (NDFD). Forecast accuracy and skill are determined relative to observations at over 3000 locations archived by MesoWest. Forecast quality is also determined relative to Rapid Update Cycle (RUC) analyses at 20-km resolution that are interpolated to the 5-km NDFD grid as well as objective analyses obtained from the Advanced Regional Prediction System Data Assimilation System that rely upon the MesoWest observations and RUC analyses. For the West as a whole, the experimental temperature forecasts issued at 0000 UTC during the 2003/04 winter season exhibit skill at lead times of 12, 24, 36, and 48 h on the basis of several verification approaches. Subgrid-scale temperature variations and observational and analysis errors undoubtedly contribute some uncertainty regarding these results. Even though the “true” values appropriate to evaluate the forecast values on the NDFD grid are unknown, it is estimated that the root-mean-square errors of the NDFD temperature forecasts are on the order of 3°C at lead times shorter than 48 h and greater than 4°C at lead times longer than 120 h. However, such estimates are derived from only a small fraction of the NDFD grid boxes. Incremental improvements in forecast accuracy as a result of forecaster adjustments to the 0000 UTC temperature grids from 144- to 24-h lead times are estimated to be on the order of 13%.

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John D. Horel
and
Chris V. Gibson

Abstract

The evolution of a major winter storm over Utah during 6–7 January 1992 is analyzed using surface and upper-air observations and satellite imagery. A mesoscale model is used to deduce the dynamical processes that took place during the storm. Output from the Nested Grid Model of the National Meteorological Center is used to specify the initial conditions and the lateral boundary conditions of the mesoscale model. Two numerical simulations that each last 12 h in duration are studied here. The first begins at 1200 UTC 6 January, while the second starts at 0000 UTC 7 January. Attention is placed on a secluded zone of warm, moist air that is located along the northern and western boundaries of the midtropospheric cyclonic circulation as it moved across Utah. Output from the second mesoscale simulation is used to explain the processes by which air in the secluded zone is lifted. These processes include large-scale ascent west of the cyclonic circulation aloft, lift provided by a shallow cold front, and orographic ascent as the low-level flow encountered the mountain ranges of western Utah.

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Lawrence B. Dunn
and
John D. Horel

Abstract

The utility of numerical model guidance produced by the National Meteorological Center has been evaluated for the forecast of convection over central Arizona during the summer monsoon season. Model output from the Nested Grid Model (NGM) and Eta model has been compared to observations taken during the 1990 field experiment referred to as the Southwest Area Monsoon Project (SWAMP).

The NGM precipitation forecasts showed little skill for events in which heavy precipitation was observed over Phoenix, Arizona. Selected events during the SWAMP period were simulated using the Eta model. Qualitative comparisons of the Eta model's precipitation forecasts with lightning data and satellite imagery suggest that the model has little skill over Arizona during the warm season. Nocturnal heavy precipitation over the lower deserts of central Arizona is nearly always preceded by afternoon convection over the mountains to the north and east. The convection over the mountains was absent in the model.

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Lawrence B. Dunn
and
John D. Horel

Abstract

Output from simulations of the Eta model are compared to special observations collected during the 1990 Southwest Area Monsoon Project (SWAMP). The emphasis is on the model's prediction of the preconvection air mass over Phoenix, Arizona, and on the model's representation of the low-level jet and moisture surge observed over southwest Arizona.

At times the model fails to capture the rapid increase in low- and mid-level moisture that is observed in the hours prior to the onset of convection. Subsequent convection is not predicted by the Eta model. In one event the model very accurately predicts the evolution of the air mass over Phoenix during the period just prior to the outbreak of severe convection. However, no convection is predicted by the model. The model seems unable to generate convection over the high terrain or lower deserts of central Arizona regardless of whether the air mass is simulated correctly.

A low-level jet feature observed over southwest Arizona during SWAMP is not correctly simulated by the Eta model. The model produces a very strong sea-breeze circulation from the Gulf of California into western Arizona in each simulation. The moisture and stability profiles associated with the sea-breeze are inconsistent with observations over southwest Arizona, which leads to a misrepresentation of the low- and midlevel moisture field over the region. Poor initial conditions in the sea surface temperature field over the Gulf of California are, at least in part, responsible for the model error.

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Daniel P. Tyndall
and
John D. Horel

Abstract

Given the heterogeneous equipment, maintenance and reporting practices, and siting of surface observing stations, subjective decisions that depend on the application tend to be made to use some observations and to avoid others. This research determines objectively high-impact surface observations of 2-m temperature, 2-m dewpoint, and 10-m wind observations using the adjoint of a two-dimensional variational surface analysis over the contiguous United States. The analyses reflect a weighted blend of 1-h numerical forecasts used as background grids and available observations. High-impact observations are defined as arising from poor observation quality, observation representativeness errors, or accurate observed weather conditions not evident in the background field. The impact of nearly 20 000 surface observations is computed over a sample of 100 analysis hours during 25 major weather events. Observation impacts are determined for each station as well as within broad network categories. For individual analysis hours, high-impact observations are located in regions of significant weather—typically, where the background field fails to define the local weather conditions. Low-impact observations tend to be ones where there are many observations reporting similar departures from the background. When averaged over the entire 100 cases, observations with the highest impact are found within all network categories and depend strongly on their location relative to other observing sites and the amount of variability in the weather; for example, temperature observations have reduced impact in urban areas such as Los Angeles, California, where observations are plentiful and temperature departures from the background grids are small.

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Brian K. Blaylock
and
John D. Horel

Abstract

The ability of the High-Resolution Rapid Refresh (HRRR) model to forecast the location of convective storms is of interest for a variety of applications. Since lightning is often present with intense convection, lightning observations from the Geostationary Lightning Mapper (GLM) on GOES-East are used to evaluate the performance of the HRRR lightning forecasts from May through September during 2018 and 2019. Model skill is presented in terms of the fractions skill score (FSS) evaluated within circular neighborhoods with radial distances from 30 to 240 km. Case studies of individual events illustrate that the HRRR lightning forecasts FSS varies from storm to storm. Mean FSS is summarized for the months with peak lightning activity (June–August) for the west, central, and east United States. Our results suggest that forecasters should use HRRR lightning forecasts to indicate general tendencies for the occurrence, region, and timing of thunderstorms in a broad region rather than expect high forecast accuracy for lightning locally. For example, when FSS is evaluated within small neighborhoods (30-km radius), mean FSS drops sharply after the first two hours of model integration in all regions and during all hours of the day. However, when evaluated within larger neighborhoods (60-km radius and larger), FSS in the western United States and northern Mexico remains high for all lead times in the late afternoon and early evening. This result is likely due to the model capturing the tendency for convection to break out over higher terrain during those hours.

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Taylor A. Gowan
and
John D. Horel

Abstract

Large wildfire outbreaks in Alaska are common from June to August. The Canadian Forest Fire Danger Rating System (CFFDRS) is used operationally by Alaskan fire managers to produce statewide fire weather outlooks and forecast guidance near active wildfires. The CFFDRS estimates of fire potential and behavior rely heavily on meteorological observations (precipitation, temperature, wind speed, and relative humidity) from the relatively small number of in situ stations across Alaska with precipitation being the most critical parameter. To improve the spatial coverage of precipitation estimates across Alaska for fire weather applications, a multisatellite precipitation algorithm was evaluated during six fire seasons (1 June–31 August 2014–19). Near-real-time daily precipitation estimates from the Integrated Multisatellite Retrievals for the Global Precipitation Mission (IMERG) algorithm were verified using 322 in situ stations across four Alaskan regions. For each region, empirical cumulative distributions of daily precipitation were obtained from station observations during each summer, and compared to corresponding distributions of interpolated values from IMERG grid points (0.1° × 0.1° grid). The cumulative distributions obtained from IMERG exhibited wet biases relative to the observed distributions for all regions, precipitation amount ranges, and summers. A bias correction approach using regional quantile mapping was developed to mitigate for the IMERG wet bias. The bias-adjusted IMERG daily precipitation estimates were then evaluated and found to produce improved gridded IMERG precipitation estimates. This approach may help to improve situational awareness of wildfire potential across Alaska and be appropriate for other high-latitude regions where there are sufficient in situ precipitation observations to help correct the IMERG precipitation estimates.

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John D. Horel
and
James T. Powell

Abstract

While many studies have examined intense rainfall and flash flooding during the North American Monsoon (NAM) in Arizona, Nevada, and New Mexico, less attention has focused on the NAMS’s extension into southwestern Utah. This study relates flash flood reports and Multi-Radar Multi-Sensor (MRMS) precipitation across southwestern Utah to atmospheric moisture content and instability analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model during the 2021–23 monsoon seasons.

MRMS quantitative precipitation estimates over southwestern Utah during summer depend largely on the areal coverage from the KICX WSR-88D radar near Cedar City, UT. Those estimates are generally consistent with the limited number of precipitation gauge reports in the region except at extended distances from the radar. A strong relationship is evident between days with widespread precipitation and afternoons with above average precipitable water (PWAT) and convective available potential energy (CAPE) estimated from HRRR analyses across the region.

Time-lagged ensembles of HRRR forecasts (initialization times from 03–06 UTC) that are 13–18 h prior to the afternoon period when convection is initiating (18–21 UTC) are useful for situational awareness of widespread precipitation events after adjusting for underprediction of afternoon CAPE. Improved skill is possible using random forest classification relying only on PWAT and CAPE to predict days experiencing excessive (upper quartile) precipitation. Such HRRR predictions may be useful for forecasters at the Salt Lake City National Weather Service Forecast Office to assist issuing flash flood potential statements for visitors to national parks and other recreational areas in the region.

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David T. Myrick
,
John D. Horel
, and
Steven M. Lazarus

Abstract

The terrain between grid points is used to modify locally the background error correlation matrix in an objective analysis system. This modification helps to reduce the influence across mountain barriers of corrections to the background field that are derived from surface observations. This change to the background error correlation matrix is tested using an analytic case of surface temperature that encapsulates the significant features of nocturnal radiation inversions in mountain basins, which can be difficult to analyze because of locally sharp gradients in temperature. Bratseth successive corrections, optimal interpolation, and three-dimensional variational approaches are shown to yield exactly the same surface temperature analysis. Adding the intervening terrain term to the Bratseth approach led to solutions that match more closely the specified analytic solution. In addition, the convergence of the Bratseth solutions to the best linear unbiased estimation of the analytic solution is faster.

The intervening terrain term was evaluated in objective analyses over the western United States derived from a modified version of the Advanced Regional Prediction System Data Assimilation System. Local adjustment of the background error correlation matrix led to improved surface temperature analyses by limiting the influence of observations in mountain valleys that may differ from the weather conditions present in adjacent valleys.

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