Valliappa Lakshmanan

The Warning Decision Support System–Integrated Information

Abstract The Warning Decision Support System–Integrated Information (WDSS-II) is the second generation of a system of tools for the analysis, diagnosis, and visualization of remotely sensed weather data. WDSS-II provides a number of automated algorithms that operate on data from multiple radars to provide information with a greater temporal resolution and better spatial coverage than their currently operational counterparts. The individual automated algorithms that have been developed using the WDSS-II infrastructure together yield a forecasting and analysis system providing real-time products useful in severe weather nowcasting. The purposes of the individual algorithms and their relationships to each other are described, as is the method of dissemination of the created products.

An Automated Technique to Quality Control Radar Reflectivity Data

Abstract Echoes in radar reflectivity data do not always correspond to precipitating particles. Echoes on radar may result from biological targets such as insects, birds, or wind-borne particles; from anomalous propagation or ground clutter; or from test and interference patterns that inadvertently seep into the final products. Although weather forecasters can usually identify and account for the presence of such contamination, automated weather-radar algorithms are drastically affected. Several horizontal and vertical features have been proposed to discriminate between precipitation echoes and echoes that do not correspond to precipitation. None of these features by themselves can discriminate between precipitating and nonprecipitating areas. In this paper, a neural network is used to combine the individual features, some of which have already been proposed in the literature and some of which are introduced in this paper, into a single discriminator that can distinguish between “good” and “bad” echoes (i...

A Real-Time, Three-Dimensional, Rapidly Updating, Heterogeneous Radar Merger Technique for Reflectivity, Velocity, and Derived Products

With the advent of real-time streaming data from various radar networks, including most Weather Surveillance Radars-1988 Doppler and several Terminal Doppler Weather Radars, it is now possible to combine data in real time to form 3D multiple-radar grids. Herein, a technique for taking the base radar data (reflectivity and radial velocity) and derived products from multiple radars and combining them in real time into a rapidly updating 3D merged grid is described. An estimate of that radar product combined from all the different radars can be extracted from the 3D grid at any time. This is accomplished through a formulation that accounts for the varying radar beam geometry with range, vertical gaps between radar scans, the lack of time synchronization between radars, storm movement, varying beam resolutions between different types of radars, beam blockage due to terrain, differing radar calibration, and inaccurate time stamps on radar data. Techniques for merging scalar products like reflectivity, and innovative, real-time techniques for combining velocity and velocity-derived products are demonstrated. Precomputation techniques that can be utilized to perform the merger in real time and derived products that can be computed from these three-dimensional merger grids are described.

Multiscale storm identification and forecast

We describe a recently developed hierarchical K-Means clustering method for weather images that can be employed to identify storms at different scales. We describe an error-minimization technique to identify movement between successive frames of a sequence and we show that we can use the K-Means clusters as the minimization template. A Kalman filter is used to provide smooth estimates of velocity at a pixel through time. Using this technique in combination with the K-Means clusters, we can identify storm motion at different scales and choose different scales to forecast based on the time scale of interest. The motion estimator has been applied both to reflectivity data obtained from the National Weather Service Radar (WSR-88D) and to cloud-top infrared temperatures obtained from GOES satellites. We demonstrate results on both these sensors.

An Efficient, General-Purpose Technique for Identifying Storm Cells in Geospatial Images

Abstract Existing techniques for identifying, associating, and tracking storms rely on heuristics and are not transferrable between different types of geospatial images. Yet, with the multitude of remote sensing instruments and the number of channels and data types increasing, it is necessary to develop a principled and generally applicable technique. In this paper, an efficient, sequential, morphological technique called the watershed transform is adapted and extended so that it can be used for identifying storms. The parameters available in the technique and the effects of these parameters are also explained. The method is demonstrated on different types of geospatial radar and satellite images. Pointers are provided on the effective choice of parameters to handle the resolutions, data quality constraints, and dynamic ranges found in observational datasets.

An Objective High-Resolution Hail Climatology of the Contiguous United States

AbstractThe threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of

MPING: Crowd-Sourcing Weather Reports for Research

1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons fo...

A separable filter for directional smoothing

The Weather Service Radar-1988 Doppler (WSR-88D) network within the United States has recently been upgraded to include dual-polarization capability. Among the expectations that have resulted from the upgrade is the ability to discriminate between different precipitation types in winter precipitation events. To know how well any such algorithm performs and whether new algorithms are an improvement, observations of winter precipitation type are needed. Unfortunately, the automated observing systems cannot discriminate between some of the more important types. Thus, human observers are needed. Yet, to deploy dedicated human observers is impractical because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations would require the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this, a simple “app” called mobile Precipitation Identification Near the Ground (mPING) was d...

A Feasibility Study for Probabilistic Convection Initiation Forecasts Based on Explicit Numerical Guidance

Anisotropic and directional filters can smooth noisy images while preserving object boundaries. Data from remote sensing instruments often have missing pixels due to geometric or power limitations. In such cases, these nonisotropic filters are very inefficient, because transform methods cannot be used when there is missing data or when logical operations need to be performed. A directional filter is introduced in this letter that retains the ability to handle missing data and is separable, making it computationally efficient. We demonstrate the directional filter on weather radar data where it can be used to smooth along fronts. Since the filter introduced here can be parameterized for scale, orientation, and aspect ratio, this filter can be used in any directional filtering application where transform methods cannot be used, but computational efficiency is desired.

An Objective Method of Evaluating and Devising Storm-Tracking Algorithms

Abstract The 2011 Spring Forecasting Experiment in the NOAA Hazardous Weather Testbed (HWT) featured a significant component on convection32 initiation (CI). As in previous HWT experiments, the CI study was a collaborative effort between forecasters and researchers, with 34 equal emphasis on experimental forecasting strategies and evaluation of prototype model guidance products. The overarching goal of the CI effort was to identify the primary challenges 36 of the CI-forecasting problem and establish a framework for additional studies and possible routine forecasting of CI. This study confirms that convection-allowing models with grid spacing ~ 4 km38 represent many aspects of the formation and development of deep convection clouds explicitly and with predictive utility. Further, it shows that automated algorithms can 40 skillfully identify the CI process during model integration. However, it also reveals that automated detection of individual convection cells, by itself, provides inadequate guidance for