A simple gravity wave drag parametrization for use in medium‐range weather forecast models

1986 ◽  
Vol 24 (2) ◽  
pp. 91-110 ◽  
Author(s):  
C. Chouinard ◽  
M. Béland ◽  
N. McFarlane
Keyword(s):  
Gravity Wave ◽  
Weather Forecast ◽  
Wave Drag ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
Forecast Models

scholarly journals An Anomaly-Based Method for Identifying Signals of Spring and Autumn Low-Temperature Events in the Yangtze River Valley, China

2015 ◽  
Vol 54 (6) ◽  
pp. 1216-1233 ◽  
Author(s):  
Weihong Qian ◽  
Yun Chen ◽  
Man Jiang ◽  
Qi Hu
Keyword(s):  
Low Temperature ◽  
Yangtze River ◽  
Weather Forecast ◽  
Yangtze River Valley ◽  
River Valley ◽  
Medium Range Weather Forecast ◽  
Weather Events ◽  
Negative Center ◽  
Medium Range ◽  
Forecast Models

AbstractAbnormally low temperature (LT) events in spring and autumn can cause severe damage to spring and autumn rice production in the mid- to lower Yangtze River valley in China. Advanced predictions of such events can help mitigate their damage. However, the current methods have limited success in describing and predicting those weather events. In this study, a new method is proposed to decompose any one of the meteorological variables into its climatic component and an anomaly, and the anomaly is used in identifying signals of the LT events. The method is used in 20 strong spring LT events and 44 autumn events during 1960–2008. The results show the advanced ability of this method to clearly describe the LT events as compared with the vague indications of such events that are produced by conventional methods currently in practice in China. In addition, the composite profile of vertical anomalies shows that a negative center of geopotential height anomalies at around 300 hPa, coexisting with a strong cold center of temperature anomalies at 850 hPa, is a signature for LT events. For the 44 autumn LT events and 20 spring LT events during 1960–2008, their early disturbances were identified up to 10.2 days and 6.9 days, respectively, before the occurrence of the LT events in the valley. This result suggests that identifying the early disturbances and extracting anomalous signals from the products of current medium-range weather forecast models may be a potential way to improve the prediction skill for LT events in the valley.

Effects of Non-orographic Gravity Wave Drag on Seasonal and Medium-range Predictions in a Global Forecast Model

2018 ◽  
Vol 54 (S1) ◽  
pp. 385-402 ◽  
Author(s):  
Hyun-Joo Choi ◽  
Ji-Young Han ◽  
Myung-Seo Koo ◽  
Hye-Yeong Chun ◽  
Young-Ha Kim ◽  
...  
Keyword(s):  
Gravity Wave ◽  
Forecast Model ◽  
Wave Drag ◽  
Medium Range

scholarly journals CFS Prediction of Winter Persistent Inversions in the Intermountain Region

2010 ◽  
Vol 25 (4) ◽  
pp. 1211-1218 ◽  
Author(s):  
Robert R. Gillies ◽  
Shih-Yu Wang ◽  
Jin-Ho Yoon ◽  
Scott Weaver
Keyword(s):  
Salt Lake ◽  
Salt Lake City ◽  
Intraseasonal Oscillation ◽  
Weather Forecast ◽  
The United States ◽  
Intermountain West ◽  
Medium Range Weather Forecast ◽  
Forecast Models ◽  
Lake City ◽  
Intermountain Region

Abstract A recent study by Gillies and others of persistent inversion events in the Intermountain West of the United States found a substantive linkage between the intraseasonal oscillation (ISO) and the development of persistent inversion events. Given that NCEP’s Climate Forecast System (CFS) has demonstrated skill in the prediction of the ISO as far out as 1 month, it was decided to examine the CFS forecast’s capability in the prediction of such winter persistent inversions. After initial analysis, a simple regression scheme is proposed that is coupled to the CFS output of geopotential height as a way to predict the occurrence of persistent inversion events for Salt Lake City, Utah. Analysis of the CFS hindcasts through the period 1981–2008 indicates that the regression coupled with the CFS can predict persistent inversion events with lead times of up to 4 weeks. The adoption of this coupled regression–CFS prediction may improve the forecasting of persistent inversion events in the Intermountain West, which is currently restricted to the more limited time span (∼10 days) of medium-range weather forecast models.

scholarly journals Scientific motivation for ADM/Aeolus mission

2018 ◽  
Vol 176 ◽  
pp. 02008
Author(s):  
Erland Källén
Keyword(s):  
Weather Forecast ◽  
Weather Forecasts ◽  
Weather And Climate ◽  
Medium Range ◽  
Forecast Models ◽  
Tropical Areas ◽  
Global Coverage ◽  
Wind Lidar ◽  
Wind Profiles

The ADM/Aeolus wind lidar mission will provide a global coverage of atmospheric wind profiles. Atmospheric wind observations are required for initiating weather forecast models and for predicting and monitoring long term climate change. Improved knowledge of the global wind field is widely recognised as fundamental to advancing the understanding and prediction of weather and climate. In particular over tropical areas there is a need for better wind data leading to improved medium range (3-10 days) weather forecasts over the whole globe.

scholarly journals Analisis Kondisi Atmosfer Terkait Siklon Tropis Pabuk Serta Pengaruhnya Terhadap Tinggi Gelombang di Perairan Kepulauan Riau

2020 ◽  
Vol 8 (2) ◽  
pp. 111
Author(s):  
Diana Cahaya Siregar ◽  
Vivi Putrima Ardah ◽  
Arlin Martha Navitri
Keyword(s):  
Tropical Cyclone ◽  
Satellite Imagery ◽  
Vertical Velocity ◽  
Atmospheric Condition ◽  
Weather Forecast ◽  
Distribution Data ◽  
Medium Range Weather Forecast ◽  
Wavewatch Iii ◽  
Medium Range ◽  
The Impact

Abstract Tropical cyclones is a synoptic scale low pressure system which can have an impact, both directly or indirectly to its traversed area. On January 1 to 6, 2019, Pabuk tropical cyclone was active on the South China Sea which its movement was to the west with its maximum wind speed was 64 knots. The aim of this study was to know the impact of Pabuk tropical cyclone to the atmospheric condition and sea wave on the Riau Islands region. This study used convective index analysis using IR1 channel of Himawari-8 satellite imagery and rainfall distribution data from rainfall observation by meteorological stations which are in the Riau Islands region. European Center for Medium-Range Weather Forecast (ECMWF) reanalysis data likes relative humidity, vertical velocity, and divergence was used to describe the atmospheric condition during the life time of Pabuk tropical cyclone. Wavewatch-III data was used to describe the condition of sea waves on the Riau Islands region. The results showed that Pabuk tropical cyclone had an impact on the growth of convective clouds which it caused the light to moderate rainfall quite evenly in the Riau Islands region. Besides, it was impact to the potential of high waves reached 4.5 meters on the northern of Anambas Sea and 7.0 meters on the north-eastern of Natuna Sea.Key words: Tropical cyclone, satellite imagery, wave height Abstrak Siklon tropis merupakan sistem tekanan rendah berskala sinoptik yang berdampak secara langsung maupun tidak langsung terhadap wilayah yang dilalui. Pada tanggal 1-6 Januari 2019, siklon tropis Pabuk muncul di wilayah Laut Cina Selatan dengan pergerakan ke arah barat dan kecepatan angin maksimumnya mencapai 64 knots. Penelitian ini dilakukan untuk mengkaji dampak yang ditimbulkan oleh siklon tropis Pabuk terhadap kondisi atmosfer dan gelombang laut di wilayah Kepulauan Riau. Penelitian ini menggunakan analisis indeks konvektif dari data citra satelit Himawari-8 kanal IR1 dan analisis sebaran hujan menggunakan data pengamatan curah hujan dari beberapa stasiun meteorologi yang ada di Kepulauan Riau. Data reanalisis European Centre for Medium-Range Weather Forecast (ECMWF) berupa kelembaban udara, vertical velocity, dan divergensi diolah untuk menggambarkan kondisi atmosfer pada masa hidup siklon tropis Pabuk. Data gelombang Wavewatch-III digunakan untuk menggambarkan kondisi gelombang laut di sekitar wilayah Kepulauan Riau. Hasil penelitian menunjukkan bahwa aktifnya siklon tropis Pabuk berdampak terhadap pertumbuhan awan konvektif yang menimbulkan hujan ringan hingga sedang yang cukup merata di wilayah Kepulauan Riau. Selain itu, berdampak juga pada potensi terjadinya gelombang tinggi mencapai 4,5 meter di sebelah utara Perairan Anambas dan 7,0 meter di sebelah timur laut Perairan Natuna.Kata Kunci: Siklon tropis, citra satelit, tinggi gelombang

scholarly journals Statistical interpretation of general circulation model: A prospect for automation of medium range local weather forecast in India

MAUSAM ◽  
2021 ◽  
Vol 47 (3) ◽  
pp. 229-236
Author(s):  
ASHOK KUMAR ◽  
PARVINDER MAINI
Keyword(s):  
General Circulation ◽  
Weather Forecasting ◽  
Monsoon Season ◽  
Circulation Model ◽  
Weather Forecast ◽  
General Circulation Models ◽  
Statistical Interpretation ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
Local Weather

The General Circulation Models (GCM), though able to provide reasonably good medium range weather forecast. have comparatively less skill in forecasting location-specific weather. This is mainly due to the poor representation of 16cal topography and other features in these models. Statistical interpretation (SI) of GCM is very essential in order to improve the location-specific medium range local weather forecast. An attempt has been made at the National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi to do this type of objective forecasting. Hence location-specific SI models are developed and a bias free forecast is obtained. One of the techniques for accomplishing this, is the Perfect Prog. Method (PPM). PPM models for precipitation (quantitative, probability, yes/no) and maximum minimum temperature are developed for monsoon season (June to August) for 10 stations in lndia. These PPM models and the output from the GCM (R-40) operational at NCMRWF, are then used to obtain the SI forecast. An indirect method based upon SI forecast and observed values of previous one or two seasons, for getting bias free forecast is explained. A comparative study of skill of bias free SI and final forecast, with the observed, issued from NCMRWF to 10 Agromet Field Units (AMFU) during monsoon season 1993, has indicated that automation of medium range local weather forecast can be achieved with the help of SI forecast.

scholarly journals Fire weather index: the skill provided by ECMWF ensemble prediction system

2020 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Claudia Vitolo ◽  
Blazej Krzeminski ◽  
Jesús San-Miguel
Keyword(s):  
Weather Forecast ◽  
Research Centre ◽  
Ensemble Prediction ◽  
Fire Weather ◽  
Prediction System ◽  
Weather Index ◽  
Fire Weather Index ◽  
Ensemble Prediction System ◽  
Medium Range Weather Forecast ◽  
Medium Range

Abstract. In the framework of the EU Copernicus program, the European Centre for Medium-range Weather Forecast (ECMWF) on behalf of the Joint Research Centre (JRC) is forecasting daily fire weather indices using its medium range ensemble prediction system. The use of weather forecast in place of local observations can extend early warnings up to 1–2 weeks allowing for greater proactive coordination of resource-sharing and mobilization within and across countries. Using one year of pre-operational service in 2017 and the fire weather index (FWI) here we assess the capability of the system globally and analyze in detail three major events in Chile, Portugal and California. The analysis shows that the skill provided by the ensemble forecast system extends to more than 10 days when compared to the use of mean climate making a case of extending the forecast range to the sub-seasonal to seasonal time scale. However accurate FWI prediction does not translate into accuracy in the forecast of fire activity globally. Indeed when all 2017 detected fires are considered, including agricultural and human induced burning, high FWI values only occurs in 50 % of the cases and only in Boreal regions. Nevertheless for very important events mostly driven by weather condition, FWI forecast provides advance warning that could be instrumental in setting up management strategies.

scholarly journals Latent Heat Flux in the Agulhas Current

2019 ◽  
Vol 11 (13) ◽  
pp. 1576 ◽  
Author(s):  
Arielle Stela Imbol Nkwinkwa N. ◽  
Mathieu Rouault ◽  
Johnny A. Johannessen
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Annual Cycle ◽  
Current System ◽  
Surface Wind ◽  
Weather Forecast ◽  
Specific Humidity ◽  
Medium Range Weather Forecast ◽  
Agulhas Current ◽  
Medium Range

In-situ observation, climate reanalyses, and satellite remote sensing are used to study the annual cycle of turbulent latent heat flux (LHF) in the Agulhas Current system. We assess if the datasets do represent the intense exchange of moisture that occurs above the Agulhas Current and the Retroflection region, especially the new reanalyses as the former, the National Centers for Environmental Prediction Reanalysis 2 (NCEP2) and the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis second-generation reanalysis (ERA-40) have lower sea and less distinct surface temperature (SST) in the Agulhas Current system due to their low spatial resolution thus do not adequately represent the Agulhas Current LHF. We use monthly fields of LHF, SST, surface wind speed, saturated specific humidity at the sea surface (Qss), and specific humidity at 10 m (Qa). The Climate Forecast System Reanalysis (CFSR), the European Centre for Medium-Range Weather Forecast fifth generation (ERA-5), and the Modern-Era Retrospective analysis for Research and Applications version-2 (MERRA-2) are similar to the air–sea turbulent fluxes (SEAFLUX) and do represent the signature of the Agulhas Current. ERA-Interim underestimates the LHF due to lower surface wind speeds than other datasets. The observation-based National Oceanography Center Southampton (NOCS) dataset is different from all other datasets. The highest LHF of 250 W/m2 is found in the Retroflection in winter. The lowest LHF (~100 W/m2) is off Port Elizabeth in summer. East of the Agulhas Current, Qss-Qa is the main driver of the amplitude of the annual cycle of LHF, while it is the wind speed in the Retroflection and both Qss-Qa and wind speed in between. The difference in LHF between product are due to differences in Qss-Qa wind speed and resolution of datasets.

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