Seasonal Runoff Forecasting Using Precipitation from Meteorological Data Assimilation Systems

The man-made development of lands for irrigated agriculture has provided benefits, but in some schemes has resulted in catastrophic problems arising from soil salinization including those in the Aral Sea Basin in central Asia and the Murray-Darling Basin in Australia. Soil salinization is a gradual process and the impact of salt intrusion is noticeable after several years of development, typically ranging from 10to30years. In central Asia salt water management has historically been based on rules of thumb with mixed results. A coupled surface and ground water model, HYDRO-GW, has been developed with the facilities to model the processes in the root zone and in the unsaturated and saturated subsurface system. The model is applied to the Kashkadarya irrigation area in Uzbekistan to assess the impact of various changes in land and water management practices on reducing salinity levels. Simplified equations have been presented which have potential application for assessing the salinization process and can be used as precursors to detailed assessment using a coupled surface and ground water model.

Several new types of satellite instrument will provide improved measurements of Earth's hydrological cycle and the humidity of the atmosphere. In an effort to make the best possible use of these data, the modeling and assimilation of humidity, clouds, and precipitation are currently the subjects of a comprehensive research program at the European Centre for Medium-Range Weather Forecasts (ECMWF). Impacts on weather prediction and climate reanalysis can be expected. The preparations for cloud and rain assimilation within ECMWF's four-dimensional variational data assimilation system include the development of linearized moist physics, the development of fast radiative transfer codes for cloudy and precipitating conditions, and a reformulation of the humidity analysis scheme. Results of model validations against in situ moisture data are presented, indicating generally good agreement—often to within the absolute calibration accuracy of the measurements. Evidence is also presented of shortcomings in ECMWF's humidity analysis, from the operational data assimilation and forecasting system in 2002, and from the recently completed ERA-40 reanalysis project. Examples are shown of biases in the data and in the model that lead to biased humidity analyses. Although these biases are relatively small, they contribute to an overprediction of tropical precipitation and to an overly intense Hadley circulation at the start of the forecast, with rapid adjustments taking place during the first 6–12 h. It is shown that with an improved humidity analysis this long-standing “spindown” problem can be reduced.

This paper evaluates the extratropical cyclones within three operational global ensembles [the 20-member Canadian Meteorological Centre (CMC), 20-member National Centers for Environmental Prediction (NCEP), and 50-member European Centre for Medium-Range Weather Forecasts (ECMWF)]. The day-0–6 forecasts were evaluated over the eastern United States and western Atlantic for the 2007–15 cool seasons (October–March) using the ECMWF’s ERA-Interim dataset as the verifying analysis. The Hodges cyclone-tracking scheme was used to track cyclones using 6-h mean sea level pressure (MSLP) data. For lead times less than 72 h, the NCEP and ECMWF ensembles have comparable mean absolute errors in cyclone intensity and track, while the CMC errors are larger. For days 4–6 ECMWF has 12–18 and 24–30 h more accuracy for cyclone intensity than NCEP and CMC, respectively. All ensembles underpredict relatively deep cyclones in the medium range, with one area near the Gulf Stream. CMC, NCEP, and ECMWF all have a slow along-track bias that is significant from 24 to 90 h, and they have a left-of-track bias from 120 to 144 h. ECMWF has greater probabilistic skill for intensity and track than CMC and NCEP, while the 90-member multimodel ensemble (NCEP + CMC + ECMWF) has more probabilistic skill than any single ensemble. During the medium range, the ECMWF + NCEP + CMC multimodel ensemble has the fewest cases (1.9%, 1.8%, and 1.0%) outside the envelope compared to ECMWF (5.6%, 5.2%, and 4.1%) and NCEP (13.7%, 10.6%, and 11.0%) for cyclone intensity and along- and cross-track positions.

Does the Aral sea merit heritage status?

In this paper, the results of an examination of precipitation forecasts for 1–30-day leads from global models run at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) during November 2007–February 2008 are presented. The performance of the model precipitation forecasts are examined in global and regional contexts, and results of a case study of precipitation variations that are associated with a moderate to strong Madden–Julian oscillation (MJO) event are presented.The precipitation forecasts from the ECMWF and NCEP operational prediction models have nearly identical temporal correlation with observed precipitation at forecast leads from 2 to 9 days over the Northern Hemisphere during the cool season, despite the higher resolution of the ECMWF operational model, while the ECMWF operational model forecasts are slightly better in the tropics and the Southern Hemisphere during the warm season. The ECMWF Re-Analysis Interim (ERA-Interim) precipitation forecasts perform only slightly worse than the NCEP operational model, while NCEP’s Climate Forecast System low-resolution coupled model forecasts perform the worst among the four models. In terms of bias, the ECMWF operational model performs the best among the four model forecasts that were examined, particularly with respect to the ITCZ regions in both the Atlantic and Pacific. Local temporal correlations that were computed on daily precipitation totals for day-2 forecasts against observations indicate that the operational models at ECMWF and NCEP perform the best during the 4-month study period, and that all of the models have low to insignificant correlations over land and over much of the tropics. They perform the best in subtropical and extratropical oceanic regions.Also presented are results that show that striking improvements have been made over the past two decades in the ability of the models to represent precipitation variations that are associated with MJO. The model precipitation forecasts exhibit the ability to characterize the evolution of precipitation variations during a moderate–strong period of MJO conditions for forecast leads as long as 10 days.

PM2.5, a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM2.5 concentrations. PM2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R2 drop. For PM2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors.

Central Asia has been rapidly changing in multiple ways over the past few decades. Increases in temperature and likely decreases in precipitation in Central Asia as the result of global climate change are making one of the most arid regions in the world even more susceptible to large-scale droughts. Global climate oscillations, such as the El Niño–Southern Oscillation, have previously been linked to observed weather patterns in Central Asia. However, until now it has been unclear how the different climate oscillations act simultaneously to affect the weather and subsequently the vegetated land surface in Central Asia. We fit well-established land surface phenology models to two versions of MODIS data to identify the land surface phenology of Central Asia between 2001 and 2016. We then combine five climate oscillation indices into one regression model and identify the relative importance of each of these indices on precipitation, temperature, and land surface phenology, to learn where each climate index has the strongest influence. Our analyses illustrate that the North Atlantic Oscillation, the East Atlantic/West Russia pattern, and the Atlantic Multi-Decadal Oscillation predominantly influence temperature in the northern part of Central Asia. We also show that the Scandinavia index and the Multivariate ENSO index both reveal significant impacts on the precipitation in this region. Thus, we conclude that the land surface phenology across Central Asia is affected by several climate modes, both those that are strongly linked to far northern weather patterns and those that are forced by southern weather patterns, making this region a ‘climate change hotspot’ with strong spatial variations in weather patterns. We also show that regional climate patterns play a significant role in Central Asia, indicating that global climate patterns alone might not be sufficient to project weather patterns and subsequent land surface changes in this region.

This paper compares upper-tropospheric humidity from the Microwave Limb Sounder (MLS) on the Upper Atmosphere Research Satellite with European Centre for Medium-Range Weather Forecasts (ECMWF) data. MLS measurements are not included in the ECMWF analyses, and so a comparison of two independent datasets is possible. The focus of this paper is on the tropical region from 1991 to 1997 when MLS measurements are available and, to be more specific, from 1991 to 1994 during the ECMWF 15-yr reanalysis period (ERA-15). It is found that the contrast between moist and dry areas, or areas of convection and subsidence, is less pronounced in ECMWF data than in MLS measurements. This result applies on the scale of both global and regional circulations and results in a semiannual cycle of high and low correlations between the two datasets that is particularly pronounced over South America. Time series show that the impact of model changes to the absolute values of humidity make long-term comparison with MLS difficult.

<p>Since 2019, ECMWF (European Centre for Medium-Range Weather Forecasts) together with EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) initiated a project named “<strong>European Weather Cloud</strong>” (https://www.europeanweather.cloud/) expected to become operational in 2022. The strategic goal of this initiative is to build and offer a <strong>community cloud infrastructure</strong> on which Member and Co‐operating States of both organizations can create and manage on demand virtual resources enabling access to the ECMWF’s Numerical Weather Predication (NWP) products and EUMETSAT’s satellite data in a timely, efficient, and configurable fashion. Moreover, one of the main goals is to involve more entities in this initiative in a joint effort to form a federation of clouds/data offered from our Member States, for the maximum benefit of the European Meteorological Infrastructure.</p><p>During the current pilot phase of the project several use cases have been defined, mostly aimed at service developers own organisations. These broad categories of use cases are:</p><ul><li>Web services exploring hosted datasets.</li> <li>Infrastructure allowing the running of an atmospheric dispersion model on ECMWF forecast data.</li> <li>Platform to support the training of machine learning models on archive datasets.</li> <li>Platform to support workshops and training courses (DWD/ICON model training, various ECMWF training courses)</li> <li>Environment facilitating research in collaboration with external partners.</li> </ul><p>Some examples of the use cases currently developed at the European Weather Cloud are:</p><ul><li>The Royal Meteorological Institute of Belgium prepares ECMWF forecast data for use in a local atmospheric dispersion model.</li> <li>The German weather service, which is already feeding maps generated by a server it deployed on the cloud into its public GeoPortal service.</li> <li>The Royal Netherlands Meteorological Institute hosts a climate explorer web application based on KNMI climate explorer data and ECMWF weather and climate reanalyses.</li> <li>EUMETSAT Numerical Weather Prediction Satellite Application Facility (NWP SAF) develops a training module will develop a training module for a fast radiative transfer model (RTTOV) based on ERA5 reanalysis data.</li> <li>EUMETSAT and ECMWF joint use case assess bias correction schemes for the assimilation of radiance data based on several satellite data time series.</li> </ul><p>During the current pilot phase of the project, both organizations have organised user and technical workshops to actively engage with the meteorological community to align the evolution of the European Weather Cloud to reflect and satisfy their goals and needs.</p><p>In this presentation, the status of the project will be analysed describing the existing infrastructure, the offered services and how these are accessed by the end-users along with examples of the existing use cases. The plans, next steps for the evolution and the transition to operations of the European Weather Cloud and its relationship with other projects and initiatives will conclude the presentation.</p>

Fog forecasting and fog nowcasting events are challenging issues especially when the fog phenomenon appears in the vicinity of airports because the reduced visibility associated with fog represent a high risk for air traffic events. Bucharest Henri Coandă International Airport (OTP, 44.57°N, 26.1°E, 95 m above sea level) is the largest airport in Romania and is located about 16 km north of Bucharest, the capital and most developed city of Romania. Its surroundings are comprised partly of residential and natural protected areas, and partly have agricultural use. Due to its geographic position, the airport is an important air traffic hub on the routes between western and eastern world destinations. In terms of numbers of flights, during the observation period analyzed here, the air traffic at OTP was significantly lowered during the spring of 2020 due to COVID-19 pandemic but soon after the restrictions were lifted and due to redirection of the flights over Ukraine after 2022, the air traffic is significantly increased in present. Data and analyses reported here cover a period of 2 decades from the beginning of 2003 to the end of 2023. Meteorological data, including fog events, relative humidity, wind speed and direction, were measured by the weather station of Romanian Air Traffic Services Administration ROMATSA R.A. Data about boundary layer and solar radiation was extracted from the public available database from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. Present study reports the analysis of the evolution of the frequency of fog events and the relationships between fog events and speed and direction of the wind, and between fog events and the relative humidity. The correlations between the boundary layer height, solar radiation and the fog events were also investigated. Bivariate polar plots revealed fog appears with higher frequency (about 32%) during cold season, from October to March, and during early morning hours. Overview of the entire data set shows in some years mono-modal distributions of the fog frequency of occurrence with respect to the local time with peaks during the night and in the early morning hours and mono-modal flat distributions in other years. We observed the fog events are correlated with dominant wind directions of east-nord-east (ENE) and west-south-west (WSW). Statistical analysis of the data also showed a prevalence of the radiation fog over the advection fog. Acknowledgement: AV was supported by the University of Bucharest, PhD research grant. AV acknowledges the partial funding from the NO Grants 2014-2021, under Project contract no. 31/2020, EEA-RO-NO-2019-0423 project. Data regarding boundary layer and solar radiation was extracted from the public available database from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. We thank ROMATSA R.A. for access to the database.

Tropical cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting.

This research evaluates the quality of high-impact rainfall forecasts across Vietnam and its sub-climate regions. The 3-day rainfall forecast products evaluated include the European Centre for Medium-Range Weather Forecasts (ECMWF) High-Resolution Integrated Forecasting System (IFS) and its downscaled outputs using the Weather Research and Forecasting (WRF) model with the Advanced Research WRF core (WRF-ARW): direct downscaling and downscaling with data assimilation. A full 5-year validation period from 2019 to 2025 was processed. The validation focused on basic rainfall thresholds and also considered the distribution of skill scores for intense events and extreme events. The validations revealed systematic errors (bias) in the models at low rainfall thresholds. The forecast skill was the lowest for northern regions, while the central regions exhibited the highest. For regions strongly affected by terrain, high-resolution downscaling with local observation data assimilation is necessary to improve the detectability of extreme events.

Abstract. The assimilation of clear-sky radiance in the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric analysis relies on the clear-sky radiances observation operator. Some of these radiances have frequencies that make them sensitive to both the surface and atmosphere. Because the atmospheric and surface analyses are currently not strongly coupled, a specific treatment of the surface is required. The observation operator specifically expects a skin temperature value at the observation location and time as well as the profiles of the atmospheric variables along the viewing path. This skin temperature is added to the control variable and optimised simultaneously with all of the atmospheric variables to produce optimal simulated radiances. We present two approaches to add the skin temperature to the control variable. In the current TOVS Control Variable (TOVSCV) approach, a series of skin temperature values per observation location is added to the control variable. Effectively, in the optimisation process, the skin temperature acts as a sink variable in observation space and is uncoupled from the skin temperature at other locations. In the novel SKin Temperature in the Extended Control Vector (SKTECV) approach, two-dimensional skin temperature fields are added to the control variable. All clear-sky radiances then participate in the optimisation of these two-dimensional fields, and the analysis produces temporally and spatially consistent skin temperature fields. We compare the two approaches over two seasons of 3 months each. Overall, there is a neutral impact of the new approach on the analysis and forecast. Moreover, there is some evidence that the contribution of the subsurface layers should be represented in the new approach for the skin temperature associated with the microwave instruments.

The paper proposes a method for correcting the precipitation forecast of the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The method first uses the ECMWF physical quantity field to draw the flow field diagram in the form of weather, and uses it as the input of the convolutional neural network(CNN) and performs feature extraction on it. Secondly, select several characteristic factors that are highly correlated with the forecast object from the many characteristic factors. Finally, the selected characteristic factors and the precipitation forecast factors of the ECMWF model are used as the input factors of the random forest regression model for modeling and forecasting. Through the correction forecast test of the next 24h precipitation at 10 forecast test stations, the results show that the ECMWF precipitation correction forecast method proposed in this paper is 15% less than the forecast method using ECMWF interpolation to the station on the MAE and RMSE indicators. And 17%. At the same time, on the TS scores of rainstorms above 24h, the revised forecast method proposed in this paper has a false alarm rate significantly lower than the forecast method of ECMWF interpolation to the station.

The El Niño–Southern Oscillation (ENSO) signal in Antarctic precipitation is evaluated using European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses and ECMWF 15-yr (1979–93) reanalyses. Operational and reanalysis datasets indicate that the ENSO teleconnection with Antarctic precipitation is manifested through a close positive correlation between the Southern Oscillation index and West Antarctic sector (75°–90°S, 120°W–180°) precipitation from the early 1980s to 1990, and a close negative correlation after 1990. However, a comparison between the operational analyses and reanalyses shows significant differences in net precipitation (P − E) due to contrasts in the mean component of moisture flux convergence into the West Antarctic sector. These contrasts are primarily due to the mean winds, which differ significantly between the operational analyses and the reanalyses for the most reliable period of overlap (1985–93). Some of the differences in flow pattern are attributed to an error in the reanalysis assimilation of Vostok station data that suppresses the geopotential heights over East Antarctica. Reanalysis geopotential heights are also suppressed over the Southern Ocean, where there is a known cold bias below 300 hPa. Deficiencies in ECMWF reanalyses result in a weaker ENSO signal in Antarctic precipitation and cause them to miss the significant upward trend in precipitation found in recent operational analyses. Ice-core analyses reflect both an upward trend in ice accumulation and the ENSO teleconnection correlation pattern seen in the operational analyses. This study confirms the results of a previous study using ECMWF operational analyses that was the first to find a strong correlation pattern between the moisture budget over the West Antarctic sector and the Southern Oscillation index.