Evaluation of the AIRS near-real-time channel selection for application to numerical weather prediction

Abstract As cloud-service providers like Google, Amazon, and Microsoft decrease costs and increase performance, numerical weather prediction (NWP) in the cloud will become a reality not only for research use but for real-time use as well. The performance of the Weather Research and Forecasting (WRF) Model on the Google Cloud Platform is tested and configurations and optimizations of virtual machines that meet two main requirements of real-time NWP are found: 1) fast forecast completion (timeliness) and 2) economic cost effectiveness when compared with traditional on-premise high-performance computing hardware. Optimum performance was found by using the Intel compiler collection with no more than eight virtual CPUs per virtual machine. Using these configurations, real-time NWP on the Google Cloud Platform is found to be economically competitive when compared with the purchase of local high-performance computing hardware for NWP needs. Cloud-computing services are becoming viable alternatives to on-premise compute clusters for some applications.

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Sensitivity of Radiative Fluxes to Aerosols in the ALADIN-HIRLAM Numerical Weather Prediction System

Atmosphere ◽

10.3390/atmos11020205 ◽

2020 ◽

Vol 11 (2) ◽

pp. 205

Author(s):

Laura Rontu ◽

Emily Gleeson ◽

Daniel Martin Perez ◽

Kristian Pagh Nielsen ◽

Velle Toll

Keyword(s):

Optical Properties ◽

Real Time ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Limited Area ◽

Prediction System ◽

Heating Rates ◽

Radiative Fluxes ◽

Numerical Weather ◽

The Impact

The direct radiative effect of aerosols is taken into account in many limited-area numerical weather prediction models using wavelength-dependent aerosol optical depths of a range of aerosol species. We studied the impact of aerosol distribution and optical properties on radiative transfer, based on climatological and more realistic near real-time aerosol data. Sensitivity tests were carried out using the single-column version of the ALADIN-HIRLAM numerical weather prediction system, set up to use the HLRADIA simple broadband radiation scheme. The tests were restricted to clear-sky cases to avoid the complication of cloud–radiation–aerosol interactions. The largest differences in radiative fluxes and heating rates were found to be due to different aerosol loads. When the loads are large, the radiative fluxes and heating rates are sensitive to the aerosol inherent optical properties and the vertical distribution of the aerosol species. In such cases, regional weather models should use external real-time aerosol data for radiation parametrizations. Impacts of aerosols on shortwave radiation dominate longwave impacts. Sensitivity experiments indicated the important effects of highly absorbing black carbon aerosols and strongly scattering desert dust.

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A Real-Time, Mesoscale Numerical Weather-Prediction System Used for Research, Teaching, and Public Service at The Pennsylvania State University

Bulletin of the American Meteorological Society ◽

10.1175/1520-0477(1990)071<0792:artmnw>2.0.co;2 ◽

1990 ◽

Vol 71 (6) ◽

pp. 792-805 ◽

Cited By ~ 15

Author(s):

Thomas T. Warner ◽

Nelson L. Seaman

Keyword(s):

Real Time ◽

Numerical Weather Prediction ◽

Public Service ◽

Weather Prediction ◽

Prediction System ◽

State University ◽

Pennsylvania State University ◽

Numerical Weather ◽

Mesoscale Numerical Weather Prediction

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A Real-Time MODIS Vegetation Product for Land Surface and Numerical Weather Prediction Models

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2013.2255059 ◽

2014 ◽

Vol 52 (3) ◽

pp. 1772-1786 ◽

Cited By ~ 23

Author(s):

Jonathan L. Case ◽

Frank J. LaFontaine ◽

Jordan R. Bell ◽

Gary J. Jedlovec ◽

Sujay V. Kumar ◽

...

Keyword(s):

Real Time ◽

Numerical Weather Prediction ◽

Land Surface ◽

Prediction Models ◽

Weather Prediction ◽

Numerical Weather ◽

Numerical Weather Prediction Models

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Influence of Near Real-Time Green Vegetation Fraction Data on the Numerical Weather Prediction in WRF over North China

10.5194/egusphere-egu2020-4551 ◽

2020 ◽

Author(s):

Bing Lu ◽

Ji-Qin Zhong ◽

Wei Wang ◽

Shi-Hao Tang ◽

Zhao-Jun Zheng

Keyword(s):

Real Time ◽

Numerical Weather Prediction ◽

North China ◽

Weather Prediction ◽

Specific Humidity ◽

Numerical Weather ◽

Vegetation Fraction ◽

Green Vegetation ◽

The Impact ◽

Mean Square Errors

<p>Green vegetation fraction (GVF) has a prominent influence on the partitioning of surface sensible and latent heat fluxes in numerical weather prediction models. However, the multi-year monthly GVF climatology, which is the most commonly-used representation of vegetation states in models, has limited ability to capture the real-time vegetation status. In our study, a near real-time (NRT) GVF dataset generated from 8-day composite of the normalized difference vegetation index (NDVI) is compared with the 10-year averaged monthly GVF provided by the Weather Research and Forecasting (WRF) model. We examine the annual and inter-annual variability of the GVF over North China in details. Many differences of the GVF between the two datasets are found over the dryland cropland and grassland areas. Two experiments using different GVF datasets are performed to assess the impact of the GVF on the forecasts of screen-level temperature and humidity for one year. The results show that using the NRT GVF can lead to a widespread reduction of 2-m temperature in the order of 0.5 &#8451;, and an increase of 2-m humidity during the warm season. An evaluation against in-situ observations displays an overall positive impact on the near surface parameter forecasts. Over the dryland cropland and grassland areas, a quantitative validation shows that the root mean square errors of 24-h forecasts decline by 9%, 10% and 6% for 2-m temperature, 2-m specific humidity and 10-m wind speed, respectively, in May of 2012. Our study demonstrates that the NRT GVF can provide a more realistic representation of vegetation state which in turn helps to improve the short-range forecasts in the arid and semiarid regions of North China.</p>

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Progress toward High-Resolution, Real-Time Radiosonde Reports

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-00169.1 ◽

2016 ◽

Vol 97 (11) ◽

pp. 2149-2161 ◽

Cited By ~ 10

Author(s):

Bruce Ingleby ◽

Patricia Pauley ◽

Alexander Kats ◽

Jeff Ator ◽

Dennis Keyser ◽

...

Keyword(s):

High Resolution ◽

Real Time ◽

Numerical Weather Prediction ◽

Meteorological Data ◽

Weather Prediction ◽

Vertical Resolution ◽

Not Given ◽

Rounding Errors ◽

Numerical Weather

Abstract Some real-time radiosonde reports are now available with higher vertical resolution and higher precision than the alphanumeric TEMP code. There are also extra metadata; for example, the software version may indicate whether humidity corrections have been applied at the station. Numerical weather prediction (NWP) centers and other users need to start using the new Binary Universal Form for Representation of Meteorological Data (BUFR) reports because the alphanumeric codes are being withdrawn. TEMP code has various restrictions and complexities introduced when telecommunication speed and costs were overriding concerns; one consequence is minor temperature rounding errors. In some ways BUFR reports are simpler: the whole ascent should be contained in a single report. BUFR reports can also include the time and location of each level; an ascent takes about 2 h and the balloon can drift 100 km or more laterally. This modernization is the largest and most complex change to the worldwide reporting of radiosonde observations for many years; international implementation is taking longer than planned and is very uneven. The change brings both opportunities and challenges. The biggest challenge is that the number and quality of the data from radiosonde ascents may suffer if the assessment of the BUFR reports and two-way communication between data producers and data users are not given the priority they require. It is possible that some countries will only attempt to replicate the old reports in the new format, not taking advantage of the benefits, which include easier treatment of radiosonde drift and a better understanding of instrument and processing details, as well as higher resolution.

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Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

Natural Hazards and Earth System Science ◽

10.5194/nhess-8-349-2008 ◽

2008 ◽

Vol 8 (2) ◽

pp. 349-357 ◽

Cited By ~ 28

Author(s):

J. Schmidt ◽

G. Turek ◽

M. P. Clark ◽

M. Uddstrom ◽

J. R. Dymond

Keyword(s):

Soil Moisture ◽

New Zealand ◽

Real Time ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Hydrologic Model ◽

Local Scale ◽

Weather Forecasts ◽

Numerical Weather ◽

Infinite Slope Stability Model

Abstract. A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

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