On the Use of QuikSCAT Scatterometer Measurements of Surface Winds for Marine Weather Prediction

2006 ◽  
Vol 134 (8) ◽  
pp. 2055-2071 ◽  
Author(s):  
Dudley B. Chelton ◽  
Michael H. Freilich ◽  
Joseph M. Sienkiewicz ◽  
Joan M. Von Ahn
Keyword(s):  
North Atlantic ◽  
Gulf Stream ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Processing System ◽  
Weather Forecast ◽  
Surface Winds ◽  
Ocean Prediction ◽  
Numerical Weather Prediction Models

Abstract The value of Quick Scatterometer (QuikSCAT) measurements of 10-m ocean vector winds for marine weather prediction is investigated from two Northern Hemisphere case studies. The first of these focuses on an intense cyclone with hurricane-force winds that occurred over the extratropical western North Pacific on 10 January 2005. The second is a 17 February 2005 example that is typical of sea surface temperature influence on low-level winds in moderate wind conditions in the vicinity of the Gulf Stream in the western North Atlantic. In both cases, the analyses of 10-m winds from the NCEP and ECMWF global numerical weather prediction models considerably underestimated the spatial variability of the wind field on scales smaller than 1000 km compared with the structure determined from QuikSCAT observations. The NCEP and ECMWF models both assimilate QuikSCAT observations. While the accuracies of the 10-m wind analyses from these models measurably improved after implementation of the QuikSCAT data assimilation, the information content in the QuikSCAT data is underutilized by the numerical models. QuikSCAT data are available in near–real time in the NOAA/NCEP Advanced Weather Interactive Processing System (N-AWIPS) and are used extensively in manual analyses of surface winds. The high resolution of the QuikSCAT data is routinely utilized by forecasters at the NOAA/NCEP Ocean Prediction Center, Tropical Prediction Center, and other NOAA weather forecast offices to improve the accuracies of wind warnings in marine forecasts.

IWV retrieval from shipborne GPS receiver on hydrographic ship Borda

2020 ◽  
Author(s):  
Olivier Bock ◽  
Pierre Bosser ◽  
Olivier Caumont ◽  
Raphael Legouge ◽  
Nicolas Laurain
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
State Of The Art ◽  
Numerical Models ◽  
Weather Prediction ◽  
Satellite Observations ◽  
Gps Data ◽  
Gps Receiver ◽  
The Past ◽  
Numerical Weather Prediction Models

<p>This work aims to provide a quick review of different experiments conducted in the past for the estimation of integrated water vapor content from shipborne GNSS receiver. This state of the art will be confronted with results obtained using GPS data acquired by the French Hydrographic Ship Borda on a cruise over Atlantic Ocean and Mediterranean Sea, from Brest to Toulon in August 2015; the estimated IWV are compared with satellite observations (MODIS) and outputs from numerical weather prediction models (ERAI, ERA5, Arpege, Arome); while differences between GPS and MODIS retrievals reach almost 4 kg/m2 in terms of RMS, agreement is generally much better with numerical models (2 up to 3 kg/m2 in terms of RMS). Use of real-time orbit and clocks product is also investigated in order to assess the performance of near real-time GPS-IWV estimation for NWP purposes. We will draw out the prospects in terms of possibilities and opportunities for the use of shipborne GNSS IWV for meteorology and climatology.</p>

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals Fengyun Meteorological Satellite Products for Earth System Science Applications

2021 ◽  
Author(s):  
Di Xian ◽  
Peng Zhang ◽  
Ling Gao ◽  
Ruijing Sun ◽  
Haizhen Zhang ◽  
...  
Keyword(s):  
Data Sharing ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Vegetation Indices ◽  
Open Data ◽  
Earth System ◽  
Inversion Algorithm

AbstractFollowing the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

scholarly journals Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Harel. B. Muskatel ◽  
Ulrich Blahak ◽  
Pavel Khain ◽  
Yoav Levi ◽  
Qiang Fu
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Radiation Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Effective Size ◽  
Asymmetry Factor ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Spectral Averaging

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

Forecasting wave height probabilities with numerical weather prediction models

2005 ◽  
Vol 32 (14-15) ◽  
pp. 1841-1863 ◽  
Author(s):  
Mark S. Roulston ◽  
Jerome Ellepola ◽  
Jost von Hardenberg ◽  
Leonard A. Smith
Keyword(s):  
Wave Height ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models

Numerical Prediction of Submesoscale Flow in the Nocturnal Stable Boundary Layer over Complex Terrain

2012 ◽  
Vol 140 (3) ◽  
pp. 956-977 ◽  
Author(s):  
Nelson L. Seaman ◽  
Brian J. Gaudet ◽  
David R. Stauffer ◽  
Larry Mahrt ◽  
Scott J. Richardson ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Stable Boundary Layers ◽  
Stable Boundary ◽  
Central Pennsylvania ◽  
Stable Conditions ◽  
Improved Model ◽  
Mean Square Errors ◽  
Numerical Weather Prediction Models

Abstract Numerical weather prediction models often perform poorly for weakly forced, highly variable winds in nocturnal stable boundary layers (SBLs). When used as input to air-quality and dispersion models, these wind errors can lead to large errors in subsequent plume forecasts. Finer grid resolution and improved model numerics and physics can help reduce these errors. The Advanced Research Weather Research and Forecasting model (ARW-WRF) has higher-order numerics that may improve predictions of finescale winds (scales <~20 km) in nocturnal SBLs. However, better understanding of the physics controlling SBL flow is needed to take optimal advantage of advanced modeling capabilities. To facilitate ARW-WRF evaluations, a small network of instrumented towers was deployed in the ridge-and-valley topography of central Pennsylvania (PA). Time series of local observations and model forecasts on 1.333- and 0.444-km grids were filtered to isolate deterministic lower-frequency wind components. The time-filtered SBL winds have substantially reduced root-mean-square errors and biases, compared to raw data. Subkilometer horizontal and very fine vertical resolutions are found to be important for reducing model speed and direction errors. Nonturbulent fluctuations in unfiltered, very finescale winds, parts of which may be resolvable by ARW-WRF, are shown to generate horizontal meandering in stable weakly forced conditions. These submesoscale motions include gravity waves, primarily horizontal 2D motions, and other complex signatures. Vertical structure and low-level biases of SBL variables are shown to be sensitive to parameter settings defining minimum “background” mixing in very stable conditions in two representative turbulence schemes.

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