European Centre Of Medium-Range Weather Forecasts Research Articles

Spatial and Temporal Variation of Subseasonal-to-Seasonal (S2S) Precipitation Reforecast Skill across the CONUS

Abstract Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resource management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash–Sutcliffe efficiency (NSE) coefficient, has been analyzed toward understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components—correlation, conditional bias, and unconditional bias—by four seasons, three lead times (1–12, 1–22, and 1–32 days), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 in. per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists, and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during the fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Postprocessing using simple model output statistics could reduce both unconditional and conditional biases to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show that efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.

Developing an efficient climate forecasting model for the spatiotemporal climate dynamics estimation and the prediction that fits the variable topography feature of the upper Blue Nile basin, Ethiopia

The spatiotemporal climate estimation and prediction are challenging and demanding for variable topography feature areas. Spatially, the upper Blue Nile basin (UBNB) is unsatisfactory owing to complex topographical features and the lack of accurate climate prediction and estimation models. Yet accurate information and reliable seasonal climate dynamics estimation and forecasting are essential in the region for controlling reservoir operation and flooding prevention. However, there is a lack of reports regarding the accuracy and predictability of the climate in the UBNB region. Therefore, this article aims to improve the Artificial Neural Network (ANN) model by adopting the Impulse Response Function (IRF) and comparing it to the Regional Climate Model (RCM) and European Centre of Medium-range Weather Forecast (ECMWF) models for spatiotemporal climate dynamics estimation and forecasting, which are reliant on the UBNB topography features. Different atmospheric parameter data are investigated from reanalysis models. A fast Fourier transform is applied to remove the redundancy of the data and avoid the computational cost. The RCM and ECMWF models are used to test the performance of ANN model prediction skills. The IRF model was applied to enhance the ANN model's climate prediction performance. A 12-month spatial variation of precipitation is analyzed. The ANN model showed a satisfactory prediction performance, better than the RCM and ECMWF models by 20 %. After increasing the ANN model performance by IRF, the prediction errors are reduced by 10.2 % for precipitation and by 7.9 % for temperature. Based on the model results, the temperature has increased over the past 40 years and is expected to continue for the coming three decades (30 years). In contrast, precipitation over the past 40 years has decreased, and a slight increment will be expected in the next eight years, from 2024 to 2029. Therefore, this model should be practiced across Ethiopia and the Globe for accurate prediction of climate patterns. Hence, clear awareness should be created for the local community by providing a scientific remedy for future climate conditions to reduce production risk.

Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India

Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result.

Global coastal low‐level wind jets revisited through the new ERA5 reanalysis

AbstractThe recent ECMWF (European Centre of Medium‐Range Weather Forecasts) reanalysis product (ERA5) is a 3D‐atmospheric dataset of unparalleled value to advance the characterization and understanding of the main global coastal low‐level jet (CLLJ) systems, due to its finer resolution (horizontal and vertical) and hourly output. Regional climate studies have revealed the added value of higher spatial and temporal resolutions of models and reanalyses for the representation of CLLJs, which play an important role in the definition of the regional climate along the Eastern Boundary Current Systems. ERA5 reveals an improvement in the representation of the wind speed and temperature vertical profiles within the boundary layer in CLLJ regions. This improvement enables a better representation of CLLJs and therefore a new view on their features. ERA5 show larger CLLJ frequencies of occurrence and larger wind speed intensities, when compared with previous reanalysis. The maximum frequency of occurrence ranges from 20% in the West Australian and 73% in the Oman CLLJs, in the summer season, which constitutes an increase of 10% compared to ERA‐Interim. Moreover, a shift for higher wind speeds is also found, where the maximum wind speed approach 30 m·s−1. Typically, CLLJs are found below 500 m above sea level, where the finer vertical discretization results clearly in a better description of the CLLJ peak height. Regarding the diurnal cycles of CLLJs frequency of occurrence, ERA5 exposes higher peaks and larger amplitudes, where the maximum hourly frequency is about 38% in the Oman CLLJ and the diurnal amplitude can vary between 7 and 14%. The diurnal maximum and minimum frequency of CLLJ occurrence can be found between 1900 and 2200 UTC, and between 1000 and 1400 LST, respectively. The momentum and temperature budget profiles reveal significant differences between CLLJ regions and emphasize the relevance of local processes in its occurrences.

Soil Moisture Estimation Synergy Using GNSS-R and L-Band Microwave Radiometry Data from FSSCat/FMPL-2

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.

Development and evaluation of an ensemble forecast/hindcast system for storm surges in the Río de la Plata Estuary

AbstractThe development and evaluation of ensemble hindcasting and forecasting systems (EHS and EFS, respectively) for storm surges in the Río de la Plata Estuary (RdP) is presented. The models were forced by atmospheric sea level pressure and 10 m winds. The ensemble forcing for the EHS was generated by temporal‐spatial shifting of the operational global control ERA5 reanalysis provided by the European Centre of Medium‐Range Weather Forecast (ECMWF), because the ERA5 associated ensemble shows too little dispersion in this area. EFS was instead based on a lead time of 4 days and forced with the 50‐member high‐resolution ensemble prediction system of the ECMWF. EHS was evaluated over a long period (the 2000–2010 decade), whereas EFS was evaluated for the ten most extreme surges that occurred during that period: five positive (which caused coastal flooding) and five negative (which affected navigation and drinking water supply) events. Based on traditional statistics (area under the ROC curve and Brier scores), both systems were assessed from a probabilistic point of view. Results show that both EHS and EFS can incorporate more than 90% of the observations in the uncertainty range. They also showed good skill in hindcasting and forecasting surges, particularly extreme events, EHS being about 20% better than the control model and EFS up to 55% better, in agreement with state‐of‐the‐art models developed for other parts of the world. Results also showed that EFS can predict most of the surge peaks with 95% confidence, with a range of uncertainty of about ±0.90 m and ±9 hr. Therefore, results encourage the implementation of EHS and EFS as useful and robust tools for future climate studies, decision makers and the general public, to improve the quality of risk management decisions by quantifying forecast uncertainty.

Augment BeiDou real-time precise point positioning using ECMWF data

The Chinese BeiDou Navigation Satellite System has shown potential for precise positioning with a comparable accuracy to that of the Global Positioning System (GPS) at the centimeter level for the horizontal component and the sub-centimeter level for the vertical component. However, a longer convergence time limits the performance of BeiDou precise point positioning (PPP) compared to the GPS PPP solutions. In this study, we applied the tropospheric delay information, derived from the European Centre of Medium-Range Weather Forecasts (ECMWF) analysis and prediction data, into the simulated real-time BeiDou-only and BeiDou/GPS PPP to augment the solutions. Observations from stations in Southeast Asia, which are capable of tracking the BeiDou constellation from the International GNSS Service (IGS) Multi-GNSS Experiment and Pilot Project (MGEX) network, are processed with different strategies: the standard PPP and the introduced ECMWF-augmented PPP with analysis and prediction data, respectively. The positioning results demonstrate that the ECMWF-augmented BeiDou-only and BeiDou/GPS PPP methods using prediction data perform as well as those using analysis data. In the case of BeiDou-only PPP scenarios, remarkable advancements of 80.6% for the convergence time are achieved by two ECMWF-augmented PPP solutions with respect to the standard PPP method. For the positioning accuracy, the two proposed augmented PPP methods attain 6.6 cm in three-dimensional (3D) accuracy when the standard PPP solution get converged (10 cm), representing a remarkable improvement of 34%. As for the north/east/up component, improvements of 14.7 and 8% for positioning accuracy are obtained for the north and east components, respectively, while a remarkable improvement of 37.3% is achieved for the vertical component. In terms of the BeiDou/GPS PPP solutions, the ECMWF-augmented PPP scenarios attain over 10% improvements in 3D accuracy in all processing session lengths. These improvements totally come from the vertical component, whereas almost no enhancements are obtained in two horizontal components.

Sensitivities of modelled water vapour in the lower stratosphere: temperature uncertainty, effects of horizontal transport and small-scale mixing

Abstract. Water vapour (H2O) in the upper troposphere and lower stratosphere (UTLS) has a significant role for global radiation. A realistic representation of H2O is therefore critical for accurate climate model predictions of future climate change. In this paper we investigate the effects of current uncertainties in tropopause temperature, horizontal transport and small-scale mixing on simulated H2O in the lower stratosphere (LS). To assess the sensitivities of simulated H2O, we use the Chemical Lagrangian Model of the Stratosphere (CLaMS). First, we examine CLaMS, which is driven by two reanalyses, from the European Centre of Medium-Range Weather Forecasts (ECMWF) ERA-Interim and the Japanese 55-year Reanalysis (JRA-55), to investigate the robustness with respect to the meteorological dataset. Second, we carry out CLaMS simulations with transport barriers along latitude circles (at the Equator, 15 and 35∘ N/S) to assess the effects of horizontal transport. Third, we vary the strength of parametrized small-scale mixing in CLaMS. Our results show significant differences (about 0.5 ppmv) in simulated stratospheric H2O due to uncertainties in the tropical tropopause temperatures between the two reanalysis datasets, JRA-55 and ERA-Interim. The JRA-55 based simulation is significantly moister when compared to ERA-Interim, due to a warmer tropical tropopause (approximately 2 K). The transport barrier experiments demonstrate that the Northern Hemisphere (NH) subtropics have a strong moistening effect on global stratospheric H2O. The comparison of tropical entry H2O from the sensitivity 15∘ N/S barrier simulation and the reference case shows differences of up to around 1 ppmv. Interhemispheric exchange shows only a very weak effect on stratospheric H2O. Small-scale mixing mainly increases troposphere–stratosphere exchange, causing an enhancement of stratospheric H2O, particularly along the subtropical jets in the summer hemisphere and in the NH monsoon regions. In particular, the Asian and American monsoon systems during a boreal summer appear to be regions especially sensitive to changes in small-scale mixing, which appears crucial for controlling the moisture anomalies in the monsoon UTLS. For the sensitivity simulation with varied mixing strength, differences in tropical entry H2O between the weak and strong mixing cases amount to about 1 ppmv, with small-scale mixing enhancing H2O in the LS. The sensitivity studies presented here provide new insights into the leading processes that control stratospheric H2O, which are important for assessing and improving climate model projections.

A Validation of Observed Meteotsunami in the West Coast of Sri Lanka with European Centre of Medium Range Weather Forecasts (ECMWF) Modelled Data

Metetsunamis are local regional events originating from perturbation of water levels by the impulse of particularly pressure drop in a disturbed atmospheric weather system and amplified to a destructive Tsunami wave with the combined effect of resonance. For the preliminary investigation a Meteotsunami event was analysed for one minute’s frequency data collected in situ sea level measuring station established in Colombo, Sri Lanka under the authority of National Aquatic Resources Research and Development Agency (NARA) with the supervision of Intergovernmental Oceanographic Commission (IOC). The contribution of disturbed weather system was analysed using wind speed (ms-1), atmospheric pressure (hPa) 10 min frequent reading from the Meteorological Department of Sri Lanka and weather station established under the bilateral project of University of Notre dame and NARA. The observed Meteotsunami event was validated using gridded modelled data (12.5 km resolution) obtained from the European Centre of Medium Range Weather Forecasts (ECMWF) and analysed using 6 hourly frequent atmospheric pressure (hPa) data, U and V at wind data at 10 m height and significant wave height. For the identification of Meteotsunami events, observed tide gauge readings were analysed in few different steps. The tidal constituents were quantified using harmonic analysis to obtained astronomical effect removed sea level variation and filtered to isolate Meteotsunami events using math lab software.According to the in situ observation data the atmospheric pressure gradient was recorded as 1008-1001 milibar hours while the modelled value dropped from 1009 to 1002 milibar hours. The observed wind speed was increased from 14.21 to 31.90 mph within 4 hours of time period and ECMWF wind speed increased from 11.02 to 19.94 mph with positive deviation. The observed wave height of Metiotsunami event was >0.3 m while model derived wave height was 0.6 m during the low tide period of the day. According to the threshold criterion suggested by Monserrat for classifying Meteotsunami, resulted wave amplitude of observation data exceeded 4 x σ, where σ is standard deviation of astronomical effect removed residual sea level time series. Further studies are required for the investigation of propagation mechanism with amplifying through the bays with the resonance effects. Further high frequency data are insufficient for the investigation of correlation with synoptic scale meteorological event.The ECMWF model is highly recommended for the prediction of disturbed atmospheric system and related ocean based extreme event forecast for prevention of ocean, atmospheric hazards and mitigation of vulnerability with the effect of global change giving real value of lives in the earth. Keywords: Sea level, Meteotsunami, Tsunami, Atmospheric, Resonance

Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction

The vegetation state can have a prominent influence on the global energy, water and carbon cycles. This has been particularly evident during extreme conditions in recent years (e.g. Europe 2003 and Russia 2010 heat waves, Horn of Africa 2010 drought, and Australia 2010 drought recovery). Weather parameters are sensitive to the vegetation state and particularly to albedo and Leaf Area Index (LAI) that controls the partitioning of the surface energy fluxes into latent and sensible fluxes, and the development of planetary boundary conditions and clouds.An optimal interpolation analysis of a satellite-based surface albedo and LAI is performed through the combination of satellite observations and derived climatologies, depending on their associated errors. The final analysis products have smoother temporal evolution than the direct observations, which makes them more appropriate for environmental and numerical weather prediction.The impact of assimilating these near-real-time (NRT) products within the land surface scheme of the European Centre of Medium-Range Weather Forecasts (ECMWF) is evaluated for anomalous years. It is shown that: (i) the assimilation of these products enables detecting/monitoring extreme climate conditions where the LAI anomaly could reach more than 50% and in wet years albedo anomaly could reach 10% , (ii) extreme NRT LAI anomalies have a strong impact on the surface fluxes, while for the albedo, which has a smaller inter-annual variability, the impact on surface fluxes is small, (iii) neutral to slightly better agreement with in-situ surface soil moisture observations and surface energy and CO2 fluxes from eddy-covariance towers is obtained, and (iv) in forecast using a land–atmosphere coupled system, the assimilation of NRT LAI reduces the near-surface air temperature and humidity errors both in wet and dry cases, while NRT albedo has a small impact, mainly in wet cases (when albedo anomalies are more noticeable).

Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations

Abstract. We applied a Regional Climate Model (RCM) to simulate precipitation and snow cover over the Himalaya, between March 2000 and December 2002. Due to its higher resolution, our model simulates a more realistic spatial variability of wind and precipitation than those of the reanalysis of the European Centre of Medium range Weather Forecast (ECMWF) used as lateral boundaries. In this region, we found very large discrepancies between the estimations of precipitation provided by reanalysis, rain gauges networks, satellite observations, and our RCM simulation. Our model clearly underestimates precipitation at the foothills of the Himalaya and in its eastern part. However, our simulation provides a first estimation of liquid and solid precipitation in high altitude areas, where satellite and rain gauge networks are not very reliable. During the two years of simulation, our model resembles the snow cover extent and duration quite accurately in these areas. Both snow accumulation and snow cover duration differ widely along the Himalaya: snowfall can occur during the whole year in western Himalaya, due to both summer monsoon and mid-latitude low pressure systems bringing moisture into this region. In Central Himalaya and on the Tibetan Plateau, a much more marked dry season occurs from October to March. Snow cover does not have a pronounced seasonal cycle in these regions, since it depends both on the quite variable duration of the monsoon and on the rare but possible occurrence of snowfall during the extra-monsoon period.

Effects of the 11 year solar cycle on middle atmospheric stationary wave patterns in temperature, ozone, and water vapor

[1] The influence of the 11 year cycle in solar irradiation on middle atmospheric stationary wave patterns in temperature, ozone, and water vapor, as indicated by the deviations from zonal mean T*, O3*, and H2O*, is investigated on the basis of time-slice simulations with the general circulation and chemistry model HAMMONIA for solar maximum and minimum conditions. For northern winter, the long-term means of the three parameters are characterized by a pronounced wave-one pattern in the middle atmosphere, but for each of the parameters with a different shift in phase with increasing height. We find a significant increase in amplitude and a horizontal shift in phase of these wave-one patterns when changing from solar minimum to maximum, i.e., regional changes of about ±2–3 K in T*, ±4%–5% in O3*, and ±2%–3% in H2O*. We demonstrate that the solar variability induces these changes by modulating the effect of zonally asymmetric radiative heating due to the stationary wave-one patterns in ozone and other absorbers and to subsequent modulations in planetary wave propagation and wave-driven transport. A comparison with ensemble means for solar maximum and minimum derived from European Centre of Medium-Range Weather Forecasts (ECMWF) Reanalysis data (ERA-40) shows reasonable agreement but also some differences in the significance and location of the solar signals, which is discussed in relation to the different setups of the two data sets. Overall, the results indicate a remarkable effect of the solar cycle on local changes in temperature, wave dynamics, and transport at northern midlatitudes and polar latitudes.

An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations

The scatterometers onboard the European Remote Sensing satellites (ERS-1 & ERS-2) and the METeorological OPerational satellite (METOP) have been shown to be useful for surface soil moisture retrieval using the so-called TU-Wien change detection method. This paper presents an improved soil moisture retrieval algorithm based on the existing TU-Wien method but with new parameterization as well as a series of modifications. The new algorithm, WAter Retrieval Package 5 (WARP5), copes with some limitations identified in the earlier method WARP4 and provides the possibility of migrating soil moisture retrieval from ERS-SCAT to METOP-ASCAT data. The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters. Cross-comparisons of WARP4 and WARP5 data sets with the Oklahoma Mesonet in situ observations and also with European Centre of Medium Range Weather Forecast (ECMWF) ReAnalysis (ERA-Interim) global modeled data show that the new algorithm has a better performance and effectively corrects retrieval errors in certain areas.

Evaluation of upper tropospheric humidity forecasts from ECMWF using AIRS and CALIPSO data

Abstract. An evaluation of the upper tropospheric humidity from the European Centre of Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) is presented. We first make an analysis of the spinup behaviour of ice supersaturation in weather forecasts. It shows that a spinup period of at least 12 h is necessary before using forecast humidity data from the upper troposphere. We compare the forecasted upper tropospheric humidity with coincident relative humidity fields retrieved from the Atmospheric InfraRed Sounder (AIRS) and with cloud vertical profiles from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The analysis is made over one year, from October 2006 to September 2007, and we discuss how relative humidity and cloud features appear both in the IFS and in the observations. The comparison with AIRS is made difficult because of the vertical resolution of the sounder and the impossibility to retrieve humidity for high cloudiness. Clear sky relative humidities show a rather good correlation whereas cloudy situations reflect more the effect of a dry bias for AIRS increasing with the relative humidity. The comparison with CALIPSO shows that the IFS predicts high relative humidity where CALIPSO finds high clouds, which supports the good quality of the ECMWF upper tropospheric cloud forecast. In a last part, we investigate the presence of ice supersaturation within low vertical resolution pressure layers by comparing the IFS outputs for high-resolution and low-resolution humidity profiles and by simulating the interpolation of humidity over radiosonde data. A new correction method is proposed and tested with these data.

Comparisons Between SAR Backscattering Coefficient and Results of a Thermodynamic Snow/Ice Model for the Baltic Sea Land-Fast Sea Ice

We have compared the time series of C-band HH-polarization backscattering coefficients (sigmadeg) of the Baltic Sea land-fast level ice with results from a 1-D high-resolution thermodynamic snow/ice model (HIGHTSI). The sigmadeg time series were obtained from ENVISAT synthetic aperture radar (SAR) images. The study period was from the middle of the winter to the early melt season, February 3-April 7, 2004. Due to the large incidence angle range of the SAR images, the sigmadeg values were divided into three subseries. In general, the HIGHTSI results greatly helped to interpret the sigmadeg behavior with changing ice and weather conditions. The modeled snow-surface temperature, cases of snow melting, and evolution of snow and ice thickness were related to the changes in sigmadeg. Equally useful information could not be obtained solely on the basis of large-scale atmospheric models. Realistic forcing data for HIGHTSI were available in the form of coastal-weather observations and model results of the European Centre of Medium-Range Weather Forecasts (ECMWF). The latter make it possible to apply HIGHTSI in the interpretation of SAR data from all ice-covered seas. There were some cases where detailed ground truth, combined with theoretical sigmadeg modeling, would have been needed for interpretation of the sigmadeg trends. A very interesting observation was the large variation of level ice sigmadeg with changing weather conditions, which complicates automatic classification of the SAR images, and thus, the algorithms must be tuned for different ice conditions. The HIGHTSI model could act as an indicator of various ice conditions for algorithm development

Inter-annual Variations of Ozone and Nitrogen Dioxide Over Europe During 1958–2003 Simulated with a Regional CTM

Inter-annual variability of surface ozone (O3) and nitrogen dioxide (NO2) over Europe has been studied over the period 1958–2003 using a three-dimensional Chemistry-Transport Model coupled to meteorological data from the ERA40 data set produced at the European Centre of Medium-range Weather Forecasts (ECMWF). Emissions and boundary conditions were kept at present levels throughout the simulation period. It was found that the annual mean NO2 concentration varies between ±50% and the summer mean O3 concentration varies between −10 and +20 percent (%) compared to the 46-year average over the model domain. There is also variation in ozone and NO2 over longer time scales. The last 22 years display high concentrations of ozone in central and south-western Europe and low concentrations in north-eastern Europe. The first 22 years display very high concentrations of NO2 over the North Sea. There is indication of trends in ozone and nitrogen dioxide but this has to be investigated further. Such information is one factor that should be taken into account when considering future control strategies.

A study of seasonal and yearly modulation of carbon dioxide sources and sinks, with a particular attention to the Boreal Atlantic Ocean

With the intention of identifying and monitoring space and time patterns of carbon dioxide sources and sinks, the seasonal fields of atmospheric CO 2 concentration over an area covering Europe, the Boreal Atlantic, and North Africa have been computed by using CO 2 observations measured at one or two remote sites in conjunction with the backward air trajectories crossing the same observation sites. The air trajectories have been calculated by means of the wind speed fields provided by the ECMWF (European Centre of Medium-range Weather Forecast, of Reading, UK) analyses (T213/L31 model) on a regular grid, while the atmospheric CO 2 concentrations have been measured at two alpine European stations, located in the free atmosphere, far from the influence of local industrial pollution. A modified version of the statistical receptor-to-source-oriented-model (hereafter, source-oriented model) of Stohl (Atmos. Environ. 30 (1998) 947), using the above-mentioned air trajectories, has then been applied to reconstruct the spatial distribution fields of atmospheric CO 2. This source-oriented methodology belongs to a family of models which are simpler and easier to use than the more powerful and widespread inverse models and can allow a reliable deduction of the location of sources and sinks of gas tracers. We have applied this kind of model in order to identify source and sink macro-regions of CO 2 over the above-mentioned area in the period 1993–1998. The CO 2 observing stations of Plateau Rosà (3480 m a.s.l., in the western Alps) and Zugspitze (2937 m, in the eastern Alps) have been considered particularly fit for this purpose, because of their location in high orography areas, allowing to monitor values of atmospheric CO 2 concentrations representative of fairly well-mixed air, not affected by some local influences (industries, urban emissions, etc.). In this way, it can be assumed that possible maxima or minima observed in the trend of measured gas concentration can be due to contaminations of the air mass during its whole travel, at some specific locations identified by the source-oriented model. The most interesting result obtained in this study is the seasonal cycle of the atmospheric CO 2 concentration found over the mid- and sub-tropical latitudes of the Boreal Atlantic Ocean and evident in all simulations. This cycle appears to be clearly related to the seasonal trend of the SST, particularly in the tropical and subtropical Atlantic regions, and is particularly evident during the warmest months (during Spring, Autumn and particularly in Summer).

Evolution of neural networks for radiative transfer calculations in the terrestrial infrared

Land surface temperature (LST) is an important component of the energy-budget of the surface. In order to determine the brightness temperature (BT) measured by a satellite for a given LST, the atmospheric influence on radiance emitted by the surface has to be accounted for. Provided that the current state of the atmosphere (vertical moisture and temperature profiles) and the surface emissivity are sufficiently well known, it is possible to calculate the BT using a radiative transfer model (RTM), e.g., Moderate Resolution Transmittance Code 3 (MODTRAN-3). RTMs do not linearise the atmospheric effect and variations of surface emissivity, elevation, and view angle (i.e., the path through the atmosphere) are readily accounted for. However, RTMs are very expensive in terms of computing time and, therefore, not well suited to simulate large quantities of data. In order to overcome this limitation, the main objective of this study is to investigate if MODTRAN-3 can be substituted by a feed-forward neural network (NN). Training and validation data consist of inputs and outputs of MODTRAN-3 for 84 TOVS Initial Guess Retrieval (TIGR) profiles in the middle latitudes (45°N to 55°N). The RTM was run for these atmospheric situations and a range of LSTs, surface elevations, and view angles. In total, 4032 radiative transfer calculations were performed. A NN developed manually for this data is evaluated using forward calculations for atmospheric profiles from the European Centre of Medium-Range Weather Forecasts (ECMWF) analyses. Grid cells of the ECMWF analyses containing clouds are identified using cloudmasks derived from Meteosat IR and VIS data. The manually developed NN is compared against a NN developed by the evolutionary algorithm “Evolutionärer Netzwerk Optimierer” (ENZO). ENZO evolves populations of NNs: it utilises mutation and crossover to optimise NN topology (layers, number of neurons, and weights) and trains (fine-tunes) the NNs with the Stuttgart Neural Network Simulator (SNNS). The NN developed by ENZO has a rms validation error (untrained TIGR situations) of 0.25 K and a rms verification error (cloud-free situations from ECMWF analyses) of 0.31 K. ENZO is shown to produce substantially smaller NNs than the manual approach: the developed NN has 58% fewer hidden neurons and about 90% fewer weights than the manually developed NN. Furthermore, the NN developed by ENZO is more robust and generalises better. A major advantage of the NN is its computational speed, which is estimated to be of the order of 10 4 times faster than MODTRAN-3.

Adaptive parallelization techniques in global weather models

Météo-France is developing a new code for operational weather forecasting that includes components both for the variational analysis of meteorological observations and for the prediction model itself. So, we need to prepare our code for the new generation of parallel machines. The parallel version of Météo-France code ARPEGE (Action de Recherche Petite Echelle Grande Echelle) uses spectral computations for the dynamic. For the spectral part, we use the strategy developed at ECMWF (European Centre of Medium-Range Weather Forecasts). The full physics scheme and some options of the semi-Lagrangian are specific to our use of ARPEGE. In this paper, we present the different strategies used for the parallelization of Météo-France forecasting code ARPEGE using the semi-Lagrangian scheme.