National Digital Forecast Database Research Articles

Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks

In this paper, we investigate mathematical models of meteorological forecasting based on the work of neural networks, which allow us to calculate presumptive meteorological parameters of the desired location on the basis of previous meteorological data. A new method of grouping neural networks to obtain a more accurate output result is proposed. An algorithm is presented, based on which the most accurate meteorological forecast was obtained based on the results of the study. This algorithm can be used in a wide range of situations, such as obtaining data for the operation of equipment in a given location and studying meteorological parameters of the location. To build this model, we used data obtained from personal weather stations of the Weather Underground company and the US National Digital Forecast Database (NDFD). Also, a Google remote learning machine was used to compare the results with existing products on the market. The algorithm for building the forecast model covered several locations across the US in order to compare its performance in different weather zones. Different methods of training the machine to produce the most effective weather forecast result were also considered.

Smart Installation Weather Warning Decision Support

Army installation commanders need timely weather information to make installation closure decisions before or during adverse weather events (e.g., hail, thunderstorms, snow, and floods). We worked with the military installation in Fort Carson, CO, and used their Weather Warning, Watch, and Advisory (WWA) criteria list to establish the foundation for our algorithm. We divided the Colorado Springs area into 2300 grids (2.5 square kilometers areas) and grouped the grids into ten microclimates, geographically and meteorologically unique regions, per pre-defined microclimate regions provided by the Fort Carson Air Force Staff Weather Officers (SWOs). Our algorithm classifies each weather event in the WWA list using the National Weather Service’s and National Digital Forecast Database’s data. Our algorithm assigns each event a criticality level: none, advisory, watch, or warning. The traffic network data highlight the importance of each road segment for travel to and from Fort Carson. The algorithm also uses traffic network data to assign weight to each grid, which enables the aggregation to the region and installation levels. We developed a weather dashboard in ArcGIS Pro to verify our algorithm and visualize the forecasted warnings for the grids and regions that are or may be affected by weather events.

Validation and Bias Correction of Forecast Reference Evapotranspiration for Agricultural Applications in Nevada

Accurate estimates of reference evapotranspiration (ET0) are critical for estimating actual crop evapotranspiration and agricultural water use. This study uses observations from the Nevada Integrated Climate and Evapotranspiration Network (NICE Net) to validate forecasts of ET0 and its driving variables from the National Weather Service’s National Digital Forecast Database (NDFD). Daily NDFD ET0 at lead times of 1 to 6 days were compared against 18 NICE Net stations. Correlations between NDFD and observations generally ranged between 0.4 and 0.9, with lower correlations at longer leads and a notable drop in skill during July and August. Systematic arid biases (high bias for temperatures and low bias for humidity) were found in NDFD with a strong warm minimum temperature bias and low vapor pressure bias most prominent during the growing season. Some of the largest relative biases were found in wind speed, although they were systematic and varied greatly by location. A case study revealed that NDFD consistently underestimates the variability found in observed minimum temperature, solar radiation, wind speed, and ET0. Cloudy days during summer were not well represented in the NDFD estimated solar radiation, which had a cascading impact on temperature, vapor pressure, and ET0 estimates. A monthly ratio-based bias-correction was applied to NDFD ET0, which reduced the root-mean squared error by 5%–30% for most locations. Bias-corrected ET0 forecasts from NDFD or other forecast systems show potential as a guide to develop weekly irrigation schedules for agricultural producers, with the ultimate goal of reducing applications of excess irrigation water.

A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast

AbstractThe Weather Research and Forecasting model (WRF) was used to produce both 9 and 3 km resolution ensemble forecasts from the deterministic Global Forecast System (GFS) model for microclimatic, agricultural regions in New York State. The forecasts were then statistically post‐processed to generate probabilistic forecasts for surface temperature (T), specific humidity (q), incoming solar radiation (SR) and precipitation (P). T was post‐processed with non‐homogeneous Gaussian regression (NGR), q and SR with truncated NGR, and P with extended logistic regression. A comparison of forecast skill was conducted between these post‐processed forecasts, the raw WRF output, the GFS forecasts and forecasts from the National Weather Service's deterministic National Digital Forecast Database (NDFD). Overall, significant improvement was observed in post‐processed WRF forecasts over all other methods for all locations and variables. Furthermore, raw WRF ensembles were found to outperform deterministic NDFD, so that if observational data are unavailable for post‐processing, dynamically downscaled WRF should be selected over deterministic, human‐altered NDFD forecasts. Finally, the 9 km post‐processed WRF had the same forecast skill as the 3 km post‐processed WRF, except for precipitation, rendering the 3 km WRF unnecessary if observational data are available, saving computational cost.

Barotropic boundary conditions and tide forcing in split-explicit high resolution coastal ocean models

A persisting problem in ocean modeling around coastal areas has been the adequate imposition of open boundary conditions, especially in the presence of complex bathymetric features commonly found in shelf seas. Ocean models with horizontal resolutions of O(1km) can generate submesoscale current variability not captured by coarser regional models which resolve mesoscale flows approximately in geostrophic and thermal-wind balance. In practice, higher-resolution systems will provide analyses with enhanced spatial detail but may be less skillful at predicting the evolution of mesoscale eddies. This study aims at assessing the role of open boundary conditions, tidal forcing and tide filtering techniques in a coastal ocean model using a split-explicit time marching scheme with horizontal resolutions of O(1km) and its effect in coastal sea level dynamics and ocean currents. Numerical experiments have been performed using the Regional Ocean Modeling System (ROMS) with different uniform horizontal resolution: 3.6km, 1.2km and 740m. Initial and lateral boundary conditions are derived from the U.S. Naval Oceanographic Office (NAVOCEANO) operational AmSeas model forecast, a 3.6-km resolution application of the regional Navy Coastal Ocean Model (NCOM) that encompasses the Gulf of Mexico and Caribbean Sea. Meteorological conditions are interpolated from the Navy’s Coastal Ocean-Atmosphere Mesoscale Prediction System (COAMPS) model with the exception of surface stresses, which are computed from a 2-km application of the Weather Research and Forecasting (WRF) model used by National Center for Environmental Protection’s (NCEP) National Digital Forecast Database (NDFD). Tidal variability is imposed in two ways: 1) by specifying the time evolution of sea level at the open boundaries and 2) spectrally by specifying the harmonic phases and amplitudes from the TPXO global inverse tide solutions at the boundaries. Sub-tidal low frequency conditions are imposed by filtering high frequencies out of the regional NCOM solution. A spectral analysis of the sea surface height and barotropic velocity is performed via Fourier’s transform, showing significant differences at higher frequencies. Tide signals are then reconstructed and removed from the open boundary condition’s (OBC’s) in 2 ways: 1) using Rich Pawlowicz’s t_tide package (i.e., classic harmonic analysis) (Pawlowicz, 2002 [1]) and 2) with traditional low-pass filters. The spectral analysis provides a comprehensive characterization of the response in coastal sea level dynamics, which is difficult to elucidate in the time domain. Modeled results of sea surface height and ocean currents are validated with NOAA tide gauges and Acoustic Doppler Current Profilers. The tide filtering approach with the low-pass filter yields significant improvement of water levels in coastal areas, and similar performance in the modeled currents. The grid sensitivity analysis shows significant reduction of error when increasing the resolution from 3.6km to 1.2km, but no further improvement is found at higher resolution. Power spectra shows that further increasing the resolution yields better agreement for higher frequencies but no significant reduction in error.

Development and validation of a coastal ocean forecasting system for Puerto Rico and the U.S. virgin islands

An ocean forecasting system has been developed for the coastal area of Puerto Rico and the U.S. Virgin Islands. This paper presents the development and validation of the Puerto Rico Operational Regional Ocean Modeling System (PRO-ROMS) developed by the Caribbean Coastal Ocean Observing System (CARICOOS), which aims at improving currently available short-term forecasts of ocean currents for this region, and to resolve sub-mesoscale variability absent from the currently operational regional systems. The coastal ocean forecasting system is based on the Regional Ocean Modeling System (ROMS), and provides a 3-day forecast of ocean currents, water levels, temperature and salinity. Initial and lateral boundary conditions are derived from the U.S. Naval Oceanographic Office (NAVOCEANO) operational AmSeas forecasting system, a 3.6-km resolution application of the regional Navy Coastal Ocean Model (NCOM) that encompasses the Gulf of Mexico and Caribbean Sea. Meteorological conditions are interpolated from the Navy’s Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) with the exception of surface stresses, which are computed from a 2-km application of the Weather Research and Forecasting (WRF) model used by National Centers for Environmental Protection’s (NCEP) National Digital Forecast Database (NDFD). Tidal variability is imposed using ROMS spectral forcing by specifying the harmonic phases and amplitudes from the TPXO global inverse tide solutions at the boundaries and sub-tidal conditions are imposed by filtering out high frequencies of barotropic variables from the lateral boundary conditions interpolated from AmSeas. Modeled results of sea surface height are validated with NOAA tide gauges, ocean currents are validated with Acoustic Doppler Current Profilers (ADCP) and High Frequency Radars (HFR). Computed statistics show improvement of the ocean current forecast in PRO-ROMS compared to AmSeas, especially at higher frequencies dominated by the tides and in small channels where the bathymetry is better represented by the higher resolution coastal ocean forecasting system.

Shining light on sky cover during a total solar eclipse

Sky cover is a unique parameter because its quantification is subject to the perspective of the observer or characteristics of the observing instrumentation. Forecasting sky cover provided a professional challenge to operational meteorologists seeking to offer a refined forecast beyond numerical weather prediction guidance along the path of totality resulting from a solar eclipse traversing North America on August 21, 2017. A routine analysis with which to monitor subtle trends in sky cover and compare sky cover forecasts is also not widely available. This contribution reviews 1-h gridded forecasts of sky cover from the United States National Weather Service (NWS) on the eclipse day and compares them with hourly satellite and surface sky observations for an area of interest over the southeastern United States. An inconsistency between the real-time mesoscale analysis (RTMA) and the NWS National Digital Forecast Database is revealed during the eclipse totality. A satellite-to-satellite comparison of the adjusted average cloud top emissivity over this same area reveals how resolution and algorithm improvements to next-generation satellite imagers may alter the RTMA of total cloud cover in the latest era of Geostationary Operational Environmental Satellites (GOES), starting with the GOES-16 Advanced Baseline Imager.

An Evaluation of NDFD Weather Forecasts for Wildland Fire Behavior Prediction

Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.

Usage of Existing Meteorological Data Networks for Parameterized Road Ice Formation Modeling

AbstractA road ice prediction model was developed on the basis of existing data networks with an objective of providing a computationally efficient method of road ice forecasting. Icing risk was separated into three distinct road ice formation mechanisms: hoarfrost, freezing fog, and frozen precipitation. Hoarfrost parameterizations were mostly gathered as presented in previous literature, with modifications incorporated to account for diffusional ice crystal growth-rate complexity. Freezing-fog parameterizations were based on previous fog typological analyses under the assumption that fog formation mechanisms are similar in above- and subfreezing temperatures. Frozen-precipitation parameterizations were primarily unique to the developed model but were also partially based on previous research. Diagnostic analyses use a synthesis of Automated Surface Observing System (ASOS), Automated Weather Observing System (AWOS), and Oklahoma Mesonet data. Prognostic analyses utilize the National Digital Forecast Database (NDFD), a 2.5-km gridded database of forecast meteorological variables output from National Weather Service Weather Forecast Offices. A frequency analysis was performed using the diagnostic parameterizations to determine general road icing risk across the state of Oklahoma. The frequency analyses aligned well with expected temporal maxima and confirmed the viability of the developed parameterizations. Further, a fog typological analysis showed the implemented freezing-fog-formation parameterizations to capture 89% of fog events. These results suggest that the developed model, identified as the Road-Ice Model (RIM), may be implemented as a robust option for analyzing the potential for road ice development based on the background meteorological environment.

Development and implementation of the BlightPro decision support system for potato and tomato late blight management

A web-based decision support system (DSS) for potato and tomato late blight management has been developed which links several models into a system that enables prediction of disease dynamics based on weather conditions, crop information, and management tactics. Growers identify the location of their production unit of interest (latitude and longitude of field) and the system automatically obtains observed weather data from the nearest available weather station, and location-specific forecast weather data from the National Weather Service – National Digital Forecast Database. The DSS uses these weather data along with crop and management information to drive disease forecasting systems and a validated mechanistic model of the disease to generate location-specific management recommendations for fungicide application. An integrated alert system allows users to receive notification of upcoming critical thresholds via e-mail or text message. This system provides producers, consultants, researchers, and educators with a tool to obtain management recommendations, evaluate disease management scenarios, explore comparative epidemiology, or function as a teaching aid. In field and computer simulation experiments, DSS-guided schedules were influenced by prevailing weather and host resistance and resulted in schedules that improved the efficiency of fungicide use and also reduced variance in disease suppression when compared to a weekly spray schedule. In situations with unfavorable weather, the DSS recommended fewer fungicide applications with no loss of disease suppression. In situations of very favorable weather, the DSS recommended more fungicide applications but with improved disease suppression. The DSS provides an interactive system that helps users maximize the efficiency of their crop protection strategy by enabling well-informed decisions.

An Examination of Wind Decay, Sustained Wind Speed Forecasts, and Gust Factors for Recent Tropical Cyclones in the Mid-Atlantic Region of the United States

Abstract In this study, several analyses were conducted that were aimed at improving sustained wind speed and gust forecasts for tropical cyclones (TCs) affecting coastal regions. An objective wind speed forecast analysis of recent TCs affecting the mid-Atlantic region was first conducted to set a benchmark for improvement. Forecasts from the National Digital Forecast Database were compared to observations and surface wind analyses in the region. The analysis suggests a general overprediction of sustained wind speeds, especially for areas affected by the strongest winds. Currently, National Weather Service Weather Forecast Offices use a software tool known as the Tropical Cyclone Forecast/Advisory (TCM) wind tool (TCMWindTool) to develop their wind forecast grids. The tool assumes linear decay in the sustained wind speeds when interpolating the National Hurricane Center 12–24-hourly TCM product to hourly grids. An analysis of postlandfall wind decay for recent TCs was conducted to evaluate this assumption. Results indicate that large errors in the forecasted wind speeds can emerge, especially for stronger storms. Finally, an analysis of gust factors for recent TCs affecting the region was conducted. Gust factors associated with weak sustained wind speeds are shown to be highly variable but average around 1.5. The gust factors decrease to values around 1.2 for wind speeds above 40 knots (kt; 1 kt = 0.51 m s−1) and are in general insensitive to the wind direction, suggesting local rather than upstream surface roughness largely dictates the gust factor at a given location. Forecasters are encouraged to increase land reduction factors used in the TCMWindTool and to modify gust factors to account for factors including the sustained wind speed and local surface roughness.

The Operational Implementation of a Great Lakes Wave Forecasting System at NOAA/NCEP*

Abstract The development of a Great Lakes wave forecasting system at NOAA’s National Centers for Environmental Prediction (NCEP) is described. The system is an implementation of the WAVEWATCH III model, forced with atmospheric data from NCEP’s regional Weather Research and Forecasting (WRF) Model [the North American Mesoscale Model (NAM)] and the National Digital Forecast Database (NDFD). Reviews are made of previous Great Lakes wave modeling efforts. The development history of NCEP’s Great Lakes wave forecasting system is presented. A performance assessment is made of model wind speeds, as well as wave heights and periods, relative to National Data Buoy Center (NDBC) measurements. Performance comparisons are made relative to NOAA’s Great Lakes Environmental Research Laboratory (GLERL) wave prediction system. Results show that 1- and 2-day forecasts from NCEP have good skill in predicting wave heights and periods. NCEP’s system provides a better representation of measured wave periods, relative to the GLERL model in most conditions. Wave heights during storms, however, are consistently underestimated by NCEP’s current operational system, whereas the GLERL model provides close agreement with observations. Research efforts to develop new wave-growth parameterizations and overcome this limitation have led to upgrades to the WAVEWATCH III model, scheduled to become operational at NCEP in 2013. Results are presented from numerical experiments made with the new wave-model physics, showing significant improvements to the skill of NCEP’s Great Lakes wave forecasting system in predicting storm wave heights.

Verification of National Weather Service spot forecasts using surface observations

Software has been developed to evaluate National Weather Service spot forecasts. Fire management officials request spot forecasts from National Weather Service Weather Forecast Offices to provide detailed guidance as to atmospheric conditions in the vicinity of planned prescribed burns as well as wildfires that do not have incident meteorologists on site. This open source software with online display capabilities is used to examine an extensive set of spot forecasts of maximum temperature, minimum relative humidity, and maximum wind speed from April 2009 through November 2013 nationwide. The forecast values are compared to the closest available surface observations at stations installed primarily for fire weather and aviation applications. The accuracy of the spot forecasts is compared to that available from the National Digital Forecast Database (NDFD). Spot forecasts for a selected prescribed burn are used to illustrate issues associated with the verification procedures. Cumulative statistics for National Weather Service County Warning Areas and for the nation are presented. Basic error and accuracy metrics for all available spot forecasts and the entire nation indicate that the skill of the spot forecasts is higher than that available from the NDFD, with the greatest improvement for maximum temperature and the least improvement for maximum wind speed.

Point-trained models in a grid environment: Transforming a potato late blight risk forecast for use with the National Digital Forecast Database

As publicly available weather forecasting datasets advance in accuracy and spatial and temporal resolution, it is relatively simple to apply these established models to new datasets but the results may deviate from what users of decision support systems have come to expect. Potato late blight risk models were some of the earliest weather-based models. This analysis compares two types of potato late blight risk models that were originally trained on location specific (point) data in Michigan. A unique system using NoSQL was developed to train, validate and implement potato late blight risk modeling using a grid data format. Each model was tested two ways; it was first deployed directly with gridded weather forecasting data as a replacement for point data, and then retrained on the gridded data. Despite consistently lower overall accuracy, the grid trained artificial neural network model was deemed of better quality for use by stakeholders because of its accuracy on days with potato late blight risk. However, the success of the model was dependent upon its retraining using the newly available data source. In the direct implementation scenario without retraining, a simpler modified-Wallin model achieved better results than the neural network model.

Error estimation of objective analysis of surface observations

As part of the Localized Aviation Model Output Statistics (MOS) Program, the Meteorological Development Laboratory is analyzing surface data reports on an hourly basis. The Bergthorsson-CressmanDoos-Glahn analysis program that is being used for gridding the MOS forecasts has been tailored to analyze surface observations. These analyses are available in the National Digital Guidance Database (NDGD) over the conterminous United States. This database is on the same grid as, and is interoperable with, the National Digital Forecast Database. It is desired to know the errors involved in these analyses. Whereas the actual errors are unknowable for several reasons, they can be estimated. On any given analysis, one would expect the error at a specific location to be a function of some knowable parameters, such as distances between the reporting locations and the grid points, the terrain roughness, the density of reporting locations, and the variability of the data values—all in the immediate vicinity. We have made analyses of surface temperature and dewpoint over the conterminous United States every fifth hour for one year. On each analysis, 20 land stations and one water station were randomly withheld from the available data. For each withheld datum, the analysis value at that site was estimated by bilinear interpolation. The differences between these interpolated values and the actual observations were related to knowable parameters through least squares regression—one relationship for land and another for water—for each variable. These regression equations were then applied, respectively, to each land and water grid point for a specific hour. This produced an estimate of the error of the analysis for each grid point for that specific time. These grids of errors are, along with the analyses, available in the NDGD. This paper describes the process and shows the results.

Prospects for flash flood forecasting in mountainous regions – An investigation of Tropical Storm Fay in the Southern Appalachians

Summary The sensitivity of Quantitative flash-Flood Estimates (QFEs) and Quantitative flash-Flood Forecasts (QFFs) to Quantitative Precipitation Estimates (QPEs) and Quantitative Precipitation Forecasts (QPFs) was investigated in three headwater catchments with different topographic and hydro-geomorphic characteristics during the passage of Tropical Storm Fay, 2008 over the Southern Appalachian Mountains in North Carolina, USA. QFEs and QFFs were generated by a high-resolution hydrologic model (250 × 250 m 2 ) with coupled surface–subsurface physics and rainfall forcing from the Next Generation Multi-sensor QPE (Q2) spatial rainfall (1 × 1 km 2 ) product, and from National Digital Forecast Database (NDFD) operational QPF product (5 × 5 km 2 ). Optimal QPE products (Q2+) were derived by assimilating rainfall observations from a high density raingauge network through adaptive bias correction. Deterministic QFEs simulated by the hydrologic model agree well with streamgauge observations (15-min intervals) regarding total water volume and peak flow with Nash–Sutcliffe (NS) coefficients 0.8–0.9, thus suggesting that the model without calibration captures well the dominant flash-flood physics. The propagation of uncertainty in storm rainfall to rainfall–runoff response was subsequently evaluated through model simulations forced by Monte Carlo replications of the QPEs to generate QFE distributions. Analysis of the joint QPE–QFE distributions shows that flood response at the catchment scale is highly non-linear, and exhibits strong dependence on basin physiography, initial soil moisture conditions (transient basin storage capacity), the space–time organization of runoff generation and conveyance mechanisms, and in particular interflow dynamics, with respect to the space–time structure of rainfall. QFFs for 6- to 1-h lead times using precipitation composites of Q2 QPE and NDFD QPF to drive the hydrology model in operational mode exhibited ubiquitous lack of skill yielding consistently negative NS scores. An experiment consisting of merging satellite-like observations into operational QPE/QPF showed significant improvement in QFF performance (e.g. 5–50% relative NS increases), especially when the timing of satellite overpass is such that it captures transient episodes of heavy rainfall during the event. Future advances in QFF remain principally constrained by progress in QPE and QPF at the spatial resolution necessary to resolve rainfall-interflow dynamics in mountainous regions.

Using the National Digital Forecast Database for model-based building controls

Abstract An accurate short-term weather forecast enhances the performance of model-based control strategies for a building. Most existing short-term forecast methods and models are based on historical archives of weather variables observed about an individual building. This practice leads to ask: 1) Does a short-term forecast model based on a collection of the past data have sufficient potential to predict future behavior, which is inherently random?; 2) If historical data is not available, or if only a limited number of weather variables is available, what are the other alternatives?; and 3) Would it be possible to derive a relevant weather data set from an incomplete historical data-driven model? The National Digital Forecast Database (NDFD) XML may answer these questions. This paper discusses an applicability of the NDFD XML for model-based building control solutions. It also presents a simplified, easily implementable, and reliable method to predict hourly global horizontal solar radiation. The short-term weather forecast method using the NDFD XML shows an outstanding performance of forecasting erratic and sporadic characteristics of weather, particularly when compared to the historical data-driven method. However, its forecast performance is not always accurate due to the inherent irregularity of weather. In order to increase robustness of model-based controls, we recommend that short-term weather forecasts should be considered as possessing scenario uncertainty . Including multiple scenarios into formulation of control problem can effectively describe scenario uncertainty when part of scenarios account for such erratic nature.

The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development

Abstract In 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis uncertainty via the Lanczos method is briefly described. The strength of the 2DVAR is illustrated by (i) its ability to analyze a June 2007 cold temperature pool over the Washington, D.C., area; (ii) its fairly good analysis of a December 2008 mid-Atlantic region high-wind event that started from a very weak first guess; and (iii) its successful recovery of the finescale moisture features in a January 2010 case study over southern California. According to a cross-validation analysis for a 15-day period during November 2009, root-mean-square error improvements over the first guess range from 16% for wind speed to 45% for specific humidity.

Regionalization in Fine-Grid GFS MOS 6-h Quantitative Precipitation Forecasts

Abstract The recent emergence of the National Digital Forecast Database as the flagship product of the National Weather Service has resulted in an increased demand for forecast guidance products on fine-mesh grids. Unfortunately, fine-grid forecasts with geographically regionalized statistical models are usually plagued by nonmeteorological discontinuities at regional boundaries. This study treats the problem in a regionalized Global Forecast System (GFS)-based model output statistics (MOS) application that produces 6-h probabilistic quantitative precipitation forecasts (PQPFs) on a 4-km grid up to 192 h in advance. The technique involves incorporating areal overlap in the geographical regionalization and weighting multiple PQPFs in region-overlap zones. The degree of overlap ranges from about 20 km along meteorologically significant regional boundaries to about 150 km at quasi-arbitrary boundaries. The forecast-weighting constants for a grid point in an overlap zone vary in direct proportion to the distances to the closest associated regional boundaries. The application of the region-overlap and forecast-weighting techniques resulted in retention of sharp PQPF gradients along meteorologically significant regional boundaries and prevention of artificial discontinuities at quasi-arbitrary boundaries. The eradication of the discontinuities in the forecast patterns was achieved without sacrificing forecast skill. While the regionalization was customized for producing high-spatial-resolution 6-h PQPFs over the contiguous United States with a specialized gridded MOS application, the region-overlap and forecast-weighting techniques may have general applicability. Also, the quality of the 6-h PQPFs was not strongly dependent on customization of the regionalization.

Validation of short and medium term operational solar radiation forecasts in the US

This paper presents a validation of the short and medium term global irradiance forecasts that are produced as part of the US SolarAnywhere (2010) data set. The short term forecasts that extend up to 6-h ahead are based upon cloud motion derived from consecutive geostationary satellite images. The medium term forecasts extend up to 6-days-ahead and are modeled from gridded cloud cover forecasts from the US National Digital Forecast Database. The forecast algorithms are validated against ground measurements for seven climatically distinct locations in the United States for 1 year. An initial analysis of regional performance using satellite-derived irradiances as a benchmark reference is also presented.