scholarly journals Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

2012 ◽  
Vol 16 (8) ◽  
pp. 2825-2838 ◽  
Author(s):  
S. Shukla ◽  
N. Voisin ◽  
D. P. Lettenmaier
Keyword(s):  
Weather Forecast ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Medium Range Weather Forecast ◽  
Weather Forecasts ◽  
Medium Range ◽  
Hydrologic Prediction ◽  
Potential Improvement

Abstract. We investigated the contribution of medium range weather forecasts with lead times of up to 14 days to seasonal hydrologic prediction skill over the conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP) based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts for runoff [SM] forecasts generally varies from 0 to 0.8 [0 to 0.5] as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

scholarly journals Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

2012 ◽  
Vol 9 (2) ◽  
pp. 1827-1857 ◽  
Author(s):  
S. Shukla ◽  
N. Voisin ◽  
D. P. Lettenmaier
Keyword(s):  
Weather Forecast ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Medium Range Weather Forecast ◽  
Weather Forecasts ◽  
Medium Range ◽  
Hydrologic Prediction ◽  
Potential Improvement

Abstract. We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 10 (2) ◽  
pp. 1987-2013 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Lead Time ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Hydrologic Prediction

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic prediction skill at seasonal lead times (i.e. 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs – primarily the state of initial soil moisture and snow) and seasonal climate forecast skill (FS). In this study we quantify the contributions of IHCs and FS to seasonal hydrologic prediction skill globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the Variable Infiltration Capacity (VIC) macroscale hydrology model, one based on Ensemble Streamflow Prediction (ESP) and another based on Reverse-ESP (rESP), both for a 47 yr reforecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts obtained from each experiment with a control simulation forced with observed atmospheric forcings over the reforecast period and estimate the ratio of Root Mean Square Error (RMSE) of both experiments for each forecast initialization date and lead time. We find that in general, the contributions of IHCs are greater than the contribution of FS over the Northern (Southern) Hemisphere during the forecast period starting in October and January (April and July). Over snow dominated regions in the Northern Hemisphere the IHCs dominate the CR forecast skill for up to 6 months lead time during the forecast period starting in April. Based on our findings we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

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 Origin and Impact of Initialization Shocks in Coupled Atmosphere–Ocean Forecasts*

2015 ◽  
Vol 143 (11) ◽  
pp. 4631-4644 ◽  
Author(s):  
David P. Mulholland ◽  
Patrick Laloyaux ◽  
Keith Haines ◽  
Magdalena Alonso Balmaseda
Keyword(s):  
Data Assimilation ◽  
Coupled System ◽  
Atmospheric Temperature ◽  
Ocean Model ◽  
Forecast Skill ◽  
Weather Forecasts ◽  
Model Component ◽  
Medium Range ◽  
Series Of Experiments ◽  
Coupled Data Assimilation

Abstract Current methods for initializing coupled atmosphere–ocean forecasts often rely on the use of separate atmosphere and ocean analyses, the combination of which can leave the coupled system imbalanced at the beginning of the forecast, potentially accelerating the development of errors. Using a series of experiments with the European Centre for Medium-Range Weather Forecasts coupled system, the magnitude and extent of these so-called initialization shocks is quantified, and their impact on forecast skill measured. It is found that forecasts initialized by separate oceanic and atmospheric analyses do exhibit initialization shocks in lower atmospheric temperature, when compared to forecasts initialized using a coupled data assimilation method. These shocks result in as much as a doubling of root-mean-square error on the first day of the forecast in some regions, and in increases that are sustained for the duration of the 10-day forecasts performed here. However, the impacts of this choice of initialization on forecast skill, assessed using independent datasets, were found to be negligible, at least over the limited period studied. Larger initialization shocks are found to follow a change in either the atmosphere or ocean model component between the analysis and forecast phases: changes in the ocean component can lead to sea surface temperature shocks of more than 0.5 K in some equatorial regions during the first day of the forecast. Implications for the development of coupled forecast systems, particularly with respect to coupled data assimilation methods, are discussed.

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.

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