Session on techniques and resources for storm-scale numerical weather prediction

The session on techniques and resources for storm-scale numerical weather prediction are reviewed. The recommendations of this group are broken down into three area: modeling and prediction, data requirements in support of modeling and prediction, and data management. The current status, modeling and technological recommendations, data requirements in support of modeling and prediction, and data management are addressed

A numerical field experiment approach for determining probabilities of microburst intensity

Several investigators had determined that some atmospheric parameters were related to the formation and severity of microbursts. For example, Caracena pointed out the relationship between a dry adiabatic lapse rate and microbursts in 'The crash of Delta Flight 191 at Dallas-Fort Worth international airport'. These early investigations led to the idea that numeric modeling of microbursts with varying atmospheric parameters might define 'signatures' that could lead to determining the probability of microburst intensity. The idea was that, by using already available sensors (such as static air temperature, pressure altitude, and radar reflectivity) onboard an aircraft, a reliable prediction of microburst existence and intensity could be formed. Such data could be used to create an 'expert meteorologist' using either artificial intelligence or other techniques that could be used in either reactive or look-ahead systems to vary sensitivity thresholds and coordinate the inputs from different detecting systems. To this end, Honeywell contracted to have the microburst simulations run. The questions to be addressed were the following: using the sensor set available to the aircraft (e.g. temperature, radar reflectivity, etc.), can we calculate the probability that (1) a microburst could be formed? and (2) that the resultant winds would be of sufficient magnitude to threaten the aircraft? Over a two year period, a data set of 1800 microburst simulations was accumulated. Verification of the microburst simulation was obtained using the results of other independent researchers and actual comparison to microburst events in Orlando and Denver. Some of the results from the simulation have already been incorporated into Honeywell's Windshear Detection and Guidance System with excellent results. Various aspects of this investigation are presented in viewgraph form

Real-time national GPS networks: Opportunities for atmospheric sensing

Real-time national Global Positioning System (GPS) networks are being established in a number of countries for atmospheric sensing. UCAR, in collaboration with participating universities, is developing one of these networks in the United States. The network, named "SuomiNet" to honor meteorological satellite pioneer Verner Suomi, is funded by the U.S. National Science Foundation. SuomiNet will exploit the recently-shown ability of ground-based GPS receivers to make thousands of accurate upper and lower atmospheric measurements per day. Phase delays induced in GPS signals by the ionosphere and neutral atmosphere can be measured with high precision simultaneously along up to a dozen GPS ray paths in the field of view. These delays can be converted into total electron content (TEC), and integrated water vapor (if surface pressure data or estimates are available), along each GPS ray path. The resulting continuous, accurate, all-weather, real-time upper and lower atmospheric data create a variety of opportunities for atmospheric research. In this letter we describe SuomiNet, its applications, and the opportunity to coordinate national real-time GPS networks to create, a global network with larger scientific and operational potential. Copy right© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences

Automatic differentiation as a tool for sensitivity analysis of a convective storm in a 3-D cloud model

The ADIFOR automatic differentiation tool is applied to a 3-D storm-scale meteorological model to generate a sensitivity-enhanced code capable of providing derivatives of all model output variables and related diagnostic (derived) parameters as a function of specified control parameters. The tangent linear approximation, applied to a deep convective storm by the first of its kind using a full-physics compressible model, is valid up to 50 min for a 1% water vapor perturbations. The result is very encouraging considering the highly nonlinear and discontinuous properties of solutions. The ADIFOR-generated code has provided valuable sensitivity information on storm dynamics. Especially, it is very efficient and useful for investigating how a perturbation inserted at earlier time propagates through the model variables at later times. However, it is computationally very expensive to be applied to the variational data assimilation, especially for 3-D meteorological models, which potentially have a large number of input variables

Correction to: Real-time national GPS networks: opportunities for atmospheric sensing (Earth, Planets and Space, (2000), 52, 11, (901-905), 10.1186/BF03352303)

Portions of this letter come from the content published in the paper by Ware et al. [1]

Techniques and resources for storm-scale numerical weather prediction

The topics discussed include the following: multiscale application of the 5th-generation PSU/NCAR mesoscale model, the coupling of nonhydrostatic atmospheric and hydrostatic ocean models for air-sea interaction studies; a numerical simulation of cloud formation over complex topography; adaptive grid simulations of convection; an unstructured grid, nonhydrostatic meso/cloud scale model; efficient mesoscale modeling for multiple scales using variable resolution; initialization of cloud-scale models with Doppler radar data; and making effective use of future computing architectures, networks, and visualization software

Prediction of Convective Storms at Convection-Resolving 1 km Resolution over Continental United States with Radar Data Assimilation: An Example Case of 26 May 2008 and Precipitation Forecasts from Spring 2009

For the first time ever, convection-resolving forecasts at 1 km grid spacing were produced in realtime in spring 2009 by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The forecasts assimilated both radial velocity and reflectivity data from all operational WSR-88D radars within a domain covering most of the continental United States. In preparation for the realtime forecasts, 1 km forecast tests were carried out using a case from spring 2008 and the forecasts with and without assimilating radar data are compared with corresponding 4 km forecasts produced in realtime. Significant positive impact of radar data assimilation is found to last at least 24 hours. The 1 km grid produced a more accurate forecast of organized convection, especially in structure and intensity details. It successfully predicted an isolated severe-weather-producing storm nearly 24 hours into the forecast, which all ten members of the 4 km real time ensemble forecasts failed to predict. This case, together with all available forecasts from 2009 CAPS realtime forecasts, provides evidence of the value of both convection-resolving 1 km grid and radar data assimilation for severe weather prediction for up to 24 hours

On the link between cold fronts and hail in Switzerland

Hail is the costliest atmospheric hazard in Switzerland, causing substantial damage to agriculture, cars and buildings every year. In this study, a 12-year statistic of objectively identified cold fronts and a radar-based hail statistic are combined to investigate the co-occurrence of cold fronts and hail in Switzerland. In a first step, an automated front identification scheme, which has previously been designed for and applied to global reanalysis data, is modified for a high-resolution regional analysis data set. This front detection method is then adapted, tested and applied to the Consortium for Small Scale Modelling (COSMO) analysis data for the extended hail season (May to September) in the years 2002–2013. The resulting cold front statistic is presented and discussed. In a second step, the frequency of cold fronts is linked to a high-resolution radar-based hail statistic to determine the relative fraction of hail initiation events in pre-frontal environments. Up to 45% of all detected hail events in north-eastern and southern Switzerland form in pre-frontal zones. Similar fractions are identified upstream of the Jura and the Black Forest mountains. The percentage of front-related hail formation is highest in regions where hail is statistically less frequent, with the exception of southern Switzerland. Furthermore, it is shown that fronts create wind-sheared environments, which are favourable for hail cells