Carrie Langston

National Mosaic and Multi-Sensor QPE (NMQ) System: Description, Results, and Future Plans

The National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or “NMQ”, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administrations National Severe Storms Laboratory, the Federal Aviation Administrations Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verif...

Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

AbstractRapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.

Brightband Identification Based on Vertical Profiles of Reflectivity from the WSR-88D

Abstract The occurrence of a bright band, a layer of enhanced reflectivity due to melting of aggregated snow, increases uncertainties in radar-based quantitative precipitation estimation (QPE). The height of the brightband layer is an indication of 0°C isotherm and can be useful in identifying areas of potential icing for aviation and in the data assimilation for numerical weather prediction (NWP). Extensive analysis of vertical profiles of reflectivity (VPRs) derived from the Weather Surveillance Radar-1988 Doppler (WSR-88D) base level data showed that the brightband signature could be easily identified from the VPRs. As a result, an automated brightband identification (BBID) scheme has been developed. The BBID algorithm can determine from a volume scan mean VPR and a background freezing level height from a numerical weather prediction model whether a bright band exists and the height of the brightband layer. The paper presents a description of the BBID scheme and evaluation results from a large dataset ...

Four-Dimensional Dynamic Radar Mosaic

Abstract Communities and many industries are affected by severe weather and have a need for real-time accurate Weather Surveillance Radar-1988 Doppler (WSR-88D) data spanning several regions. To fulfill this need the National Severe Storms Laboratory has developed a Four-Dimensional Dynamic Grid (4DDG) to accurately represent discontinuous radar reflectivity data over a continuous 4D domain. The objective is to create a seamless, rapidly updating radar mosaic that is well suited for use by forecasters in addition to advance radar applications such as qualitative precipitation estimates. Several challenges are associated with creating a 3D radar mosaic given the nature of radar data and the spherical coordinates of radar observations. The 4DDG uses spatial and temporal weighting schemes to overcome these challenges, with the intention of applying minimal smoothing to the radar data. Previous multiple radar mosaics functioned in two or three dimensions using a variety of established weighting schemes. The 4...

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

AbstractThis study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QP...

Probabilistic precipitation rate estimates with ground‐based radar networks

The uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale. By using the WSR-88D radar network and gauge data sets across the conterminous US, an investigation of this subject has been carried out within the framework of the NOAA/NSSL ground radar-based Multi-Radar Multi-Sensor (MRMS) QPE system. A new method is proposed and called PRORATE for probabilistic QPE using radar observations of rate and typology estimates. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between radar reflectivity and the corresponding “true” precipitation. The model acknowledges the uncertainty arising from many factors operative at the radar measurement scale and from the correction algorithm. Ensembles of reflectivity-to-precipitation rate relationships accounting explicitly for precipitation typology were derived at a 5 min/1 km scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and type. The model components were estimated on the basis of a 1 year long data sample over the CONUS. This PQPE model provides the basis for precipitation probability maps and the generation of radar precipitation ensembles. Maps of the precipitation exceedance probability for specific thresholds (e.g., precipitation return periods) are computed. Precipitation probability maps are accumulated to the hourly time scale and compare favorably to the deterministic QPE. As an essential property of precipitation, the impact of the temporal correlation on the hourly accumulation is examined. This approach to PQPE can readily apply to other systems including space-based passive and active sensor algorithms.

A Physically Based Precipitation–Nonprecipitation Radar Echo Classifier Using Polarimetric and Environmental Data in a Real-Time National System

AbstractPolarimetric radar observations provide information regarding the shape and size of scatterers in the atmosphere, which help users to differentiate between precipitation and nonprecipitation radar echoes. Identifying and removing nonprecipitation echoes in radar reflectivity fields is one critical step in radar-based quantitative precipitation estimation. An automated algorithm based on reflectivity, correlation coefficient, and temperature data is developed to perform reflectivity data quality control through a set of physically based rules. The algorithm was tested with a large number of real data cases across different geographical regions and seasons and showed a high accuracy (Heidke skill score of 0.83) in segregating precipitation and nonprecipitation echoes. The algorithm was compared with two other operational and experimental reflectivity quality control methodologies and showed a more effective removal of nonprecipitation echoes and a higher computational efficiency. The current methodo...

A Real-Time Algorithm for Merging Radar QPEs with Rain Gauge Observations and Orographic Precipitation Climatology

AbstractHigh-resolution, accurate quantitative precipitation estimation (QPE) is critical for monitoring and prediction of flash floods and is one of the most important drivers for hydrological forecasts. Rain gauges provide a direct measure of precipitation at a point, which is generally more accurate than remotely sensed observations from radar and satellite. However, high-quality, accurate precipitation gauges are expensive to maintain, and their distributions are too sparse to capture gradients of convective precipitation that may produce flash floods. Weather radars provide precipitation observations with significantly higher resolutions than rain gauge networks, although the radar reflectivity is an indirect measure of precipitation and radar-derived QPEs are subject to errors in reflectivity–rain rate (Z–R) relationships. Further, radar observations are prone to blockages in complex terrain, which often result in a poor sampling of orographically enhanced precipitation. The current study aims at a ...

A Statistical Approach to Mitigating Persistent Clutter in Radar Reflectivity Data

Although there are several effective signal processing methods for identifying and removing radar echoes due to ground clutter, the need to mitigate persistent clutter in radar moment data still exists if such techniques were not applied during data collection and the time series data are not available. A statistical approach to creating a clutter map from “found data”, i.e., data not specifically collected in clear air is described in this paper. Different methods of mitigating ground clutter are then compared using an information theory statistical approach and the best mitigation approach chosen. The technique described in this paper allows for the mitigation of persistent ground clutter returns in archived data where signal processing techniques have not been applied or have been conservatively applied. It is also helpful for correcting mobile radar data where the creation of a clear-air clutter map is impractical. Accordingly, the technique is demonstrated in each of the above situations.

The CI-Flow Project: A System for Total Water Level Prediction from the Summit to the Sea

The objective of the Coastal and Inland Flooding Observation and Warning (CI-FLOW) project is to prototype new hydrometeorologic techniques to address a critical NOAA service gap: routine total water level predictions for tidally influenced watersheds. Since February 2000, the project has focused on developing a coupled modeling system to accurately account for water at all locations in a coastal watershed by exchanging data between atmospheric, hydrologic, and hydrodynamic models. These simulations account for the quantity of water associated with waves, tides, storm surge, rivers, and rainfall, including interactions at the tidal/surge interface. Within this project, CI-FLOW addresses the following goals: i) apply advanced weather and oceanographic monitoring and prediction techniques to the coastal environment; ii) prototype an automated hydrometeorologic data collection and prediction system; iii) facilitate interdisciplinary and multiorganizational collaborations; and iv) enhance techniques and techn...