scholarly journals A Map Projection System Suitable for Large-scale Numerical Weather Prediction

1957 ◽  
Vol 35A (0) ◽  
pp. 262-267 ◽  
Author(s):  
N. A. Phillips
Keyword(s):  
Numerical Weather Prediction ◽  
Large Scale ◽  
Weather Prediction ◽  
Projection System ◽  
Map Projection ◽  
Numerical Weather

scholarly journals Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction

2019 ◽  
Vol 147 (4) ◽  
pp. 1107-1126 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Louis Wicker ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Systems ◽  
State Variables ◽  
Numerical Weather

Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

Atmospheric angular momentum forecasts as novel tests of global numerical weather prediction models

1991 ◽  
Vol 334 (1633) ◽  
pp. 55-92 ◽  
Keyword(s):  
Angular Momentum ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Angular Momentum ◽  
Axial Component ◽  
Practical Applications ◽  
Numerical Weather ◽  
Three Components

The global circulation of the terrestrial atmosphere exhibits fluctuations of considerable amplitude in all three components of its total angular momentum on interannual, seasonal and shorter timescales. The fluctuations must be intimately linked with nonlinear barotropic and baroclinic energetic conversion processes throughout the whole atmosphere and it is advocated that studies of routinely produced determinations of atmospheric angular momentum (AAM) changes be incorporated into systematic diagnostic investigations of large-scale atmospheric flows, AAM fluctuations are generated by dynamical interactions between the atmosphere and the underlying planet. These excite tiny but measurable compensating fluctuations in the rotation vector of the massive solid Earth, thereby ensuring conservation of the angular momentum of the whole system. Forecasts and analyses of changes in AAM from the output of a global numerical weather prediction (GNWP) model constitute a stringent test of the model. Successful forecasts of the axial com ponent of AAM, and hence of irregular non-tidal components of short-term changes in the Earth’s rotation, would find practical applications in various areas of astronomy and geodesy, such as spacecraft navigation. Reported in this paper are the main findings of intercomparisons of analyses and forecasts of changes in all three components of AAM obtained from the operational GNWP models at the United Kingdom Meteorological Office (UKMO) and the European Centre for Medium Range Weather Forecasts (ECMWF), over the period covering the two years 1987 and 1988. Included in the results obtained is the finding that useful forecasts of changes in the axial component of AAM can be made out to 5 days and even slightly longer.

scholarly journals Creating Truth Data to Quantify the Accuracy of Cloud Forecasts from Numerical Weather Prediction and Climate Models

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 177 ◽  
Author(s):  
Keith Hutchison ◽  
Barbara Iisager
Keyword(s):  
Satellite Imagery ◽  
Numerical Weather Prediction ◽  
Cloud Cover ◽  
Large Scale ◽  
Climate Models ◽  
Cloud Model ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Spectral Bands ◽  
Cloud Forecast

Clouds are critical in mechanisms that impact climate sensitivity studies, air quality and solar energy forecasts, and a host of aerodrome flight and safety operations. However, cloud forecast accuracies are seldom described in performance statistics provided with most numerical weather prediction (NWP) and climate models. A possible explanation for this apparent omission involves the difficulty in developing cloud ground truth databases for the verification of large-scale numerical simulations. Therefore, the process of developing highly accurate cloud cover fraction truth data from manually generated cloud/no-cloud analyses of multispectral satellite imagery is the focus of this article. The procedures exploit the phenomenology to maximize cloud signatures in a variety of remotely sensed satellite spectral bands in order to create accurate binary cloud/no-cloud analyses. These manual analyses become cloud cover fraction truth after being mapped to the grids of the target datasets. The process is demonstrated by examining all clouds in a NAM dataset along with a 24 h WRF cloud forecast field generated from them. Quantitative comparisons with the cloud truth data for the case study show that clouds in the NAM data are under-specified while the WRF model greatly over-predicts them. It is concluded that highly accurate cloud cover truth data are valuable for assessing cloud model input and output datasets and their creation requires the collection of satellite imagery in a minimum set of spectral bands. It is advocated that these remote sensing requirements be considered for inclusion into the designs of future environmental satellite systems.

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