European Centre For Medium-Range Weather Forecasts Forecasts Research Articles

Spatial-temporal characteristics analysis of solar irradiance forecast errors in Europe and North America

Accurate photovoltaic (PV) power forecast is crucial to power systems. Solar irradiance forecast is the fundamentals of PV power forecast. Current researches merely focus on improving the forecast accuracy. There hasn't been comprehensive study on the spatial-temporal characteristics of solar irradiance forecast errors. Thus, this paper analyzed the error characteristics of solar irradiance forecast provided by European Centre for Medium-Range Weather Forecasts (ECMWF), one of the most accurate numerical weather prediction (NWP) products. ECMWF's forecasts were compared with ERA5 solar irradiance reanalysis data to reveal the error distribution characteristics. Four-year (2017–2020) data from the geographical region bounded by 63°N, −126°W, 21°S, and 36°E (covering most parts of Europe and North America) were studied. Experiments show that solar irradiance forecast errors peak at noon of the local time. Furthermore, correlations between solar irradiance forecast errors and other weather variables were also revealed. Experiments show that solar irradiance forecast errors have negative correlation with low cloud cover forecast errors and positive correlation with air temperature forecast errors. The possible reasons for the correlation relationship were also analyzed in detail. This paper systematically reveals the spatial-temporal characteristics of solar irradiance forecast errors and provides a useful guideline for solar PV system operation.

ECMWF and UK Met Office Offshore Blowing Winds: Impact of Horizontal Resolution and Coastal Orography

AbstractWe analyze the performance of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and UK Met Office (UKMO) meteorological models in predicting offshore blowing wind in coastal areas. Our attention is mainly on the Mediterranean coast, up to 200 km distance from the shore. We compare forecast neutral winds with Advanced Scatterometer measurements. The results indicate that the ECMWF forecasts systematically underestimate wind speed with respect to scatterometer data, while the UKMO model tends to overestimate. A cross‐analysis suggests that, better than fetch, model biases are a function of the model horizontal discretization, hence of the number of grid steps the wind runs over the sea. The steepness and roughness of the land orography before entering the sea, together with the related drag parameters, appear to have a strong role in determining the coastal offshore wind values. Surface drag over land tends to reduce the related wind speed, and it takes a few grid points to adjust to the smooth sea surface conditions. This is supported by a detailed study with a high resolution grid, but different orography resolutions. In these simulations, practically identical coastal wind speed distributions are found when the subgrid orography schemes are switched off. Vertical cross‐sections of potential temperature and wind in bora and mistral conditions illustrate the strong role of gravity waves, wind channeling and turbulent diffusion in coastal numerical weather prediction. Besides local wave modeling, this may be relevant for offshore wind farms, typically situated within 5–50 km from the coast.

Euro-Atlantic Weather Regimes and Their Modulation by Tropospheric and Stratospheric Teleconnection Pathways in ECMWF Reforecasts

Abstract This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts. Significance Statement Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts.

Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction

This work is concerned with optimally combining quantiles of several post-processed versions of ensemble solar forecasts, which is new in this field. Numerical weather prediction (NWP) serves grid integration of solar energy by issuing dynamical ensemble irradiance forecasts. However, these ensemble members often suffer from under-dispersion, which motivates statistical calibration via quantile regression (QR) or ensemble model output statistics (EMOS). Given the numerous variants of QR and EMOS, it is generally unclear which variant offers the best performance under what situation, which further motivates combining quantile forecasts. A framework for combining solar forecasts in the form of quantiles is proposed, and a constrained quantile regression averaging scheme is used to exemplify the framework. Using the strictly proper pinball loss, ensemble irradiance forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System are first post-processed using five QR variants and five EMOS variants, and then combined through a linear program. It is found that combining quantiles is an effective strategy that can further improve the calibrated ECMWF forecasts across all locations herein considered.

Performance and validation of the YHGSM global spectral model coupled with the WAM model

AbstractIn this article, we describe an implementation of two‐way coupling between the Yin–He Global Spectral Model (YHGSM) and the Wave Model (WAM). The YHGSM atmosphere model gives the surface air water density and 10‐m wind for the WAM model, while the WAM model provides the Charnock coefficient as a response. The effects on the meteorological aptitude of the two‐way coupled system are assessed statistically. The coupled model exhibits a 75% significant positive impact on both the northern (NthH) and the southern hemisphere (SthH) for all metrics at almost all pressure levels. Typhoon track assessments of the coupled model will be implemented later. The results show that the coupling of the WAM model benefits typhoon landfall prediction. Finally, we make a comprehensive comparison of the significant wave height (SWH) between the YHGSM–WAM coupled model and the European Centre for Medium‐Range Weather Forecasts (ECMWF) model with buoy data from the Chinese Ocean Monitoring Network. The comparison shows that the YHGSM–WAM coupled model exhibits more accuracy and predictive stability of SWH forecasts against buoy observations than the ECMWF forecasts. The above results indicate that the YHGSM–WAM coupled model possesses superior performance to uncoupled models.

Forecasting annual maximum water level for the Negro River at Manaus using dynamical seasonal predictions

Early and skilful prediction of the Negro River maximum water levels at Manaus is critical for effective mitigation measures to safeguard lives and livelihoods. Using dynamical seasonal prediction hindcasts, from six prediction centres, we investigate extending the lead time of previously developed statistical models, which issue forecasts in March for Manaus. The original statistical forecast models used observed rainfall as the major predictor. We advance the capability to issue skilful forecasts earlier, in February. We develop ensemble forecasts by combining predictor data from observations and seasonal hindcasts. We compare those forecasts against the original statistical forecast models and forecasts using the observed climatology or persistence of predictors. The ensemble-mean forecasts, issued in February, using European Centre for Medium-Range Weather Forecasts (ECMWF) hindcast input, perform similarly as the original forecasts issued in March and gain one month of lead time. The ECMWF-based ensemble forecasts skilfully predict the likelihood of water levels exceeding the severe flood level of 29 m. Forecast performance reduces and ensemble spread increases with increasing lead time from February to January. We conclude that forecasts for Manaus maximum water levels can be produced using combined input from observations and real-time ECMWF forecasts.

Evaluation of Tianji and ECMWF high-resolution precipitation forecasts for extreme rainfall event in Henan in July 2021

The extreme rainfall event of July 17 to 22, 2021 in Henan Province, China, led to severe urban waterlogging and flood disasters. This study investigated the performance of high-resolution weather forecasts in predicting this extreme event and the feasibility of weather forecast-based hydrological forecasts. To achieve this goal, high-resolution precipitation forecasts from the Tianji weather system and the forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated with the spatial verification metrics of structure, amplitude, and location. The results showed that Tianji weather forecasts accurately predicted the amplitude of 12-h accumulated precipitation with a lead time of 12 h. The location and structure of the rainfall areas in Tianji forecasts were closer to the observations than ECMWF forecasts. Tianji hourly precipitation forecasts were also more accurate than ECMWF hourly forecasts, especially at lead times shorter than 8 h. The precipitation forecasts were used as the inputs to a hydrological model to evaluate their hydrological applications. The results showed that the runoff forecasts driven by Tianji weather forecasts could effectively predict the extreme flood event. The runoff forecasts driven by Tianji forecasts were more accurate than those driven by ECMWF forecasts in terms of amplitude and location. This study demonstrates that high-resolution weather forecasts and corresponding hydrological forecasts can provide valuable information in advance for disaster warnings and leave time for people to act on the event. The results encourage further hydrological applications of high-resolution weather forecasts, such as Tianji weather forecasts, in the future.

The representation of the trade winds in ECMWF forecasts and reanalyses during EUREC4A

Abstract. The characterization of systematic forecast errors in lower-tropospheric winds is an essential component of model improvement. This paper is motivated by a global, long-standing surface bias in the operational medium-range weather forecasts produced with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Over the tropical oceans, excessive easterly flow is found. A similar bias is found in the western North Atlantic trades, where the EUREC4A field campaign provides an unprecedented wealth of measurements. We analyze the wind bias in the IFS and ERA5 reanalysis throughout the entire lower troposphere during EUREC4A. The wind bias varies greatly from day to day, resulting in root mean square errors (RMSEs) up to 2.5 m s−1, with a mean wind speed bias up to −1 m s−1 near and above the trade inversion in the forecasts and up to −0.5 m s−1 in reanalyses. These biases are insensitive to the assimilation of sondes. The modeled zonal and meridional winds exhibit a diurnal cycle that is too strong, leading to a weak wind speed bias everywhere up to 5 km during daytime but a wind speed bias below 2 km at nighttime that is too strong. Removing momentum transport by shallow convection reduces the wind bias near the surface but leads to stronger easterly near cloud base. The update in moist physics in the newest IFS cycle (cycle 47r3) reduces the meridional wind bias, especially during daytime. Below 1 km, modeled friction due to unresolved physical processes appears to be too strong but is (partially) compensated for by the dynamics, making this a challenging coupled problem.

The Challenge of Arctic Sea Ice Thickness Prediction by ECMWF on Subseasonal Time Scales

AbstractA recent study has provided the first thorough assessment of subseasonal Arctic sea ice edge prediction in operational forecast systems. However, the corresponding assessment of the Arctic sea ice thickness (SIT) is still lacking. Here, the Arctic SIT reforecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) on subseasonal time scales are evaluated for the first time using a well‐developed SIT reanalysis data set. The results show that ECMWF forecasts of Arctic SIT are more skillful than persistence forecast (PFs) during the transition seasons for lead times longer than 30 days, indicating the advantages of dynamical prediction. However, ECMWF suffers from large initial SIT errors and has lower skill than a PF, especially from March to June. Thus, subseasonal Arctic SIT predictions still face many challenges, and improving the mean SIT in the model and assimilating satellite‐estimated SIT in the ocean analysis would significantly improve SIT forecasts.

Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants

To contribute for improved operational strategies of concentrating solar power plants with accurate forecasts of direct normal irradiance, this work describes the use of several post-processing methods on numerical weather prediction. Focus is given to a multivariate regression model that uses measured irradiance values from previous hours to improve next-hour predictions, which can be used to refine daily strategies based on day-ahead predictions. Short-term forecasts provided by the Integrated Forecasting System, the global model from the European Centre for Medium-Range Weather Forecasts (ECMWF), are used together with measurements in southern Portugal. As a nowcasting tool, the proposed regression model significantly improves hourly predictions with a skill score of ≈0.84 (i.e. an increase of ≈27.29% towards the original hourly forecasts). Using previous-day measured availability to improve next-day forecasts, the model shows a skill score of ≈0.78 (i.e. an increase of ≈6% towards the original forecasts), being further improved if larger sets of data are used. Through a power plant simulator (i.e. the System Advisor Model), a preliminary economic analysis shows that using improved hourly predictions of electrical energy allows to enhance a power plant’s profit in ≈0.44 M€/year, as compared with the original forecasts. Operational strategies are proposed accordingly.

Towards an unbiased stratospheric analysis

AbstractThe standard, strong‐constraint formulation of 4D‐Var is designed to correct for random, zero‐mean errors from the model forecast and the observations. However, significant systematic errors are generated by the forecast models used in global numerical weather prediction (NWP), and the Integrated Forecast System (IFS) model of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is no exception. To deal with this type of error, a modification of the standard 4D‐Var algorithm, weak‐constraint 4D‐Var, has long been proposed and was implemented in operations at ECMWF in 2009. In the original implementation, the model error corrections were only active in the upper stratosphere, as a full application of the algorithm would lead to unacceptable degradation of forecast scores. Thus the impact of weak‐constraint 4D‐Var on ECMWF analyses and forecasts has always been small heretofore.In a companion article, it was shown through theoretical considerations and experiments with a simplified model that weak‐constraint 4D‐Var can correct accurately for model errors and initial‐state errors only when background and model errors have different spatial scales and when the observing system has a component, sufficient in number, that is unbiased and spatially homogeneous. This article presents new diagnostics based on radio occultation (RO) temperature retrievals and shows how those assumptions are verified in the IFS model. A new model‐error covariance matrix is derived and reduces the analysis and first‐guess temperature bias in the stratosphere by up to 50%. We also present how the model error tendencies diagnosed by weak‐constraint 4D‐Var can be used to reduce mean errors in medium‐range forecasts and discuss how they can also be applied in a reanalysis context to improve the fidelity and continuity of climate reconstructions.

The influence of DACCIWA radiosonde data on the quality of ECMWF analyses and forecasts over southern West Africa

AbstractDuring the DACCIWA (Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa) field campaign ∼900 radiosondes were launched from 12 stations in southern West Africa from 15 June to 31 July 2016. Subsequently, data‐denial experiments were conducted using the Integrated Forecasting System of the European Centre for Medium‐range Weather Forecasts (ECMWF) to assess the radiosondes' impact on the quality of analyses and forecasts. As observational reference, satellite‐based estimates of rainfall and outgoing long‐wave radiation (OLR) as well as the radiosonde measurements themselves are used. With regard to the analyses, the additional observations show positive impacts on winds throughout the troposphere and lower stratosphere, while large lower‐tropospheric cold and dry biases are hardly reduced. Nonetheless, downstream, that is farther inland from the radiosonde stations, we find a significant increase (decrease) in low‐level night‐time temperatures (monsoon winds) when incorporating the DACCIWA observations, suggesting a possible linkage via weaker cold air advection from the Gulf of Guinea. The associated lower relative humidity leads to reduced cloud cover in the DACCIWA analysis. Closer to the coast and over Benin and Togo, DACCIWA observations increase low‐level specific humidity and precipitable water, possibly due to changes in advection and vertical mixing. During daytime, differences between the two analyses are generally smaller at low levels. With regard to the forecasts, the impact of the additional observations is lost after a day or less. Moderate improvements occur in low‐level wind and temperature but also in rainfall over the downstream Sahel, while impacts on OLR are ambiguous. The changes in precipitation appear to also affect high‐level cloud cover and the tropical easterly jet. The overall rather small observation impact suggests that model and data assimilation deficits are the main limiting factors for better forecasts in West Africa. The new observations and physical understanding from DACCIWA can hopefully contribute to reducing these issues.

Comparing GEFS, ECMWF, and Postprocessing Methods for Ensemble Precipitation Forecasts over Brazil

Abstract This study compares the performance of Global Ensemble Forecast System (GEFS) and European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation ensemble forecasts in Brazil and evaluates different analog-based methods and a logistic regression method for postprocessing the GEFS forecasts. The numerical weather prediction (NWP) forecasts were evaluated against the Physical Science Division South America Daily Gridded Precipitation dataset using both deterministic and probabilistic forecasting evaluation metrics. The results show that the ensemble precipitation forecasts performed commonly well in the east and poorly in the northwest of Brazil, independent of the models and the postprocessing methods. While the raw ECMWF forecasts performed better than the raw GEFS forecasts, analog-based GEFS forecasts were more skillful and reliable than both raw ECMWF and GEFS forecasts. The choice of a specific postprocessing strategy had less impact on the performance than the postprocessing itself. Nonetheless, forecasts produced with different analog-based postprocessing strategies were significantly different and were more skillful and as reliable and sharp as forecasts produced with the logistic regression method. The approach considering the logarithm of current and past reforecasts as the measure of closeness between analogs was identified as the best strategy. The results also indicate that the postprocessing using analog methods with long-term reforecast archive improved raw GEFS precipitation forecasting skill more than using logistic regression with short-term reforecast archive. In particular, the postprocessing dramatically improves the GEFS precipitation forecasts when the forecasting skill is low or below zero.

Weather forecasts to enhance an Irish grass growth model

Abstract Grass growth models have retrospectively predicted grass growth in Ireland using weather observations. However, to predict future grass growth to aid farm management, weather forecasts are necessary inputs. The Moorepark St. Gilles grass growth model (MoSt GGM) is mechanistic and was developed to predict perennial ryegrass growth on any Irish farm. To date, it has used local farm information, (retrospective) weather data and management factors to predict daily paddock-level grass growth. Here, we include weather forecasts in the MoSt GGM and assess its performance through two studies: daily grass growth predictions at four nitrogen fertiliser application levels using weather forecasts up to ten days in advance were compared with those using weather observations; and the GGM predictions for an Irish dairy farm using observed and forecast weather were compared with on-farm grass growth observations from 2013 to 2016. In the first study, all weather inputs captured the rise in grass growth predictions with higher fertiliser application. Based on the Root Mean Squared Error (RMSE), European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts outperformed a forecast based on climatological averages as GGM inputs up to six days in advance, and up to ten days in advance after bias correction. In the second study, ECMWF forecasts were the best weather forecast to predict grass growth since they captured weather variability well and did not require the local weather observations necessary for bias corrections. Weather forecasts are useful inputs to the MoSt GGM, and yield accurate weekly predictions that could aid management decisions.

Verification and bias correction of ECMWF forecasts for Irish weather stations to evaluate their potential usefulness in grass growth modelling

ABSTRACTTypical weather in Ireland provides conditions favourable for sustaining grass growth throughout most of the year. This affords grass based farming a significant economic advantage due to the low input costs associated with grass production. To optimize the productivity of grass based systems, farmers must manage the resource over short time scales. While research has been conducted into developing predictive grass growth models for Ireland to support on‐farm decision making, short term weather forecasts have not yet been incorporated into these models. To assess their potential for use in predictive grass growth models, deterministic forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) were verified for lead times up to 10 days using observations from 25 Irish weather stations. Forecasts of air temperature variables were generally precise at all lead times, particularly up to 7 days. Verification of ECMWF soil temperature forecasts is limited, but here they were shown to be accurate at all depths and most precise at greater depths such as 50 cm. Rainfall forecasts performed well up to approximately 5 days. Seven bias correction techniques were assessed to minimize systematic biases in the forecasts. Based on the root mean squared error values, no large improvement was identified for rainfall forecasts on equivalent ECMWF forecasts, but the optimum bias corrections improved air and soil temperature forecasts greatly. Overall, the results demonstrated that forecasts predict observations accurately up to approximately a week in advance and therefore could prove valuable in grass growth prediction at farm level in Ireland.

Analysis of Fractions Skill Score properties for random precipitation fields and ECMWF forecasts

The Fractions Skill Score (FSS) is a spatial verification measure that is used for assessing the performance of precipitation forecasts from numerical weather prediction models. Previous studies have shown that the FSS is able to give a direct measure of the error in the placement of the rain. This article takes the approach further and derives analytical expressions and uses Monte‐Carlo simulations for randomly positioned observed and forecast rainfall to reveal further characteristics of the FSS in both infinite and bounded domains. It reveals that the definition of an FSS value that determines the minimum scale at which a forecast should be deemed ‘useful’ (useful forecast criteria) is a meaningful concept and shows how this value increases with increasing fractional rainfall coverage. A study of real forecast data is also presented using 8 years of European Centre for Medium‐Range Weather Forecasts (ECMWF) model forecasts, out to a lead time of 9 days, over domains of differing sizes covering parts of Europe and North Africa. The FSS is examined using different strategies for dealing with the domain boundary and is compared with the analytical study. The findings give practical guidance on how to use the FSS. For most situations a FSS value of >0.5 serves as a good indicator of a useful forecast. The choice of domain size for rainfall forecast verification should consider the typical spatial errors of the forecast. For a domain that is large compared to the typical spatial error, the boundaries have little adverse affect, but this is not the case if the spatial errors start to become comparable to the size of the domain. The evaluation of ECMWF forecasts reveals the extent of the spatial errors that emerge for medium‐range forecasts and show the value of verifying those forecasts using the FSS over an appropriately sized region.

Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting

Isolated power systems need to generate all the electricity demand with their own renewable resources. Among the latter, solar energy may account for a large share. However, solar energy is a fluctuating source and the island power grid could present an unstable behavior with a high solar penetration. Global Horizontal Solar Irradiance (GHI) forecasting is an important issue to increase solar energy production into electric power system. This study is focused in hourly GHI forecasting from 1 to 6 h ahead. Several statistical models have been successfully tested in GHI forecasting, such us autoregressive (AR), autoregressive moving average (ARMA) and Artificial Neural Networks (ANN). In this paper, ANN models are designed to produce intra-day solar forecasts using ground and exogenous data. Ground data were obtained from two measurement stations in Gran Canaria Island. In order to improve the results obtained with ground data, satellite GHI data (from Helioclim-3) as well as solar radiation and Total Cloud Cover forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as additional inputs of the ANN model. It is shown that combining exogenous data (satellite and ECMWF forecasts) with ground data further improves the accuracy of the intra-day forecasts.

On mesoscale analysis and ASCAT ambiguity removal

The two‐dimensional variational ambiguity removal (2DVAR) method provides a spatial analysis of the sampled ocean vector winds to resolve the local Advanced Scatterometer (ASCAT) dual wind vector ambiguity. Like other variational meteorological data assimilation systems in numerical weather prediction (NWP), 2DVAR combines ASCAT observations with prior NWP background information, in this case from the European Centre for Medium‐range Weather Forecasts (ECMWF). Although 2DVAR is generally effective, it may select the wrong ambiguity under certain conditions, e.g. when the background mislocates frontal areas or low‐pressure centres, or when it misses convective systems. The relative influence of the ASCAT and ECMWF wind fields in the resulting 2DVAR analysis field can be controlled by adjusting the background error spatial correlation structure, and the background and/or observation error variances. In this article an adaptive 2DVAR approach is proposed to improve ASCAT ambiguity removal, using background error spatial correlations estimated from the autocorrelation of observed scatterometer wind components minus ECMWF forecasts, and using observation and background errors estimated from triple collocation analysis on collocated buoy, ASCAT and ECMWF data. The triple collocations are segregated into several categories according to the ASCAT‐derived parameters that have proven to be effective in detecting the correct position of frontal lines and low‐pressure centres. Verification using a typical cyclone case and collocated ASCAT and buoy winds shows that the 2DVAR analysis as well as the ASCAT ambiguity removal is improved significantly by putting more weight on the ASCAT observations using empirically determined spatial background error structure functions and situation‐dependent observation/background error variances.

Skill and economic benefits of dynamical downscaling of ECMWF ENSEMBLE seasonal forecast over southern Africa with RegCM4

ABSTRACTThis study presents an assessment of the skill and economic benefits of a seasonal forecast carried out by nine‐member ensembles of regional and global models over southern Africa for the period of 1991–2002. The regional ensemble seasonal hindcasts were conducted at a resolution of 25 km by driving RegCM4 with the corresponding ensembles of the European Centre for Medium‐Range Weather Forecasts (ECMWF) seasonal hindcasts. Skill and cost/loss ratio analysis were carried out on the probabilistic hindcasts to assess how much added value a regional model would bring over the driving ECMWF forecasts. RegCM4 shows an added value over most areas of southeastern Africa where the skill of the global circulation model (GCM) is already positive. In terms of economic benefits, RegCM4 brings additional benefits for southeastern Africa either by benefiting more users with interest in larger cost/loss ratio or by providing a higher economic value for users with small cost/loss ratio. Additional RegCM4 simulations with the same resolution were performed using a near ‘perfect’ boundary conditions with ‘continuous (climate)’ and ‘reinitialized’ modes to test the impact of land surface initialization/ spin up time on the seasonal mean precipitation simulation. Evaluation of the ‘perfect’ boundary forced simulations reveals that the regional model not only generates finer spatial‐scale features that are missing in the ECMWF GCM, in terms of the December to April (DJFMA) climatology, but also reduced the wet bias of the driving GCM. Land surface initialization is found to have less impact on the DJFMA seasonal mean rainfall for forecasts initialized on 1 November, suggesting that 1 month is enough to recover from the shock of poor soil moisture initialization.

Cloud‐induced uncertainties in AIRS and ECMWF temperature and specific humidity

AbstractThe uncertainties of the Atmospheric Infrared Sounder (AIRS) Level 2 version 6 specific humidity (q) and temperature (T) retrievals are quantified as functions of cloud types by comparison against Integrated Global Radiosonde Archive radiosonde measurements. The cloud types contained in an AIRS/Advanced Microwave Sounding Unit footprint are identified by collocated Moderate Resolution Imaging Spectroradiometer retrieved cloud optical depth (COD) and cloud top pressure. We also report results of similar validation of q and T from European Centre for Medium‐Range Weather Forecasts (ECMWF) forecasts (EC) and retrievals from the AIRS Neural Network (NNW), which are used as the initial state for AIRS V6 physical retrievals. Differences caused by the variation in the measurement locations and times are estimated using EC, and all the comparisons of data sets against radiosonde measurements are corrected by these estimated differences. We report in detail the validation results for AIRS GOOD quality control, which is used for the AIRS Level 3 climate products. AIRS GOOD quality q reduces the dry biases inherited from the NNW in the middle troposphere under thin clouds but enhances dry biases in thick clouds throughout the troposphere (reaching −30% at 850 hPa near deep convective clouds), likely because the information contained in AIRS retrievals is obtained in cloud‐cleared areas or above clouds within the field of regard. EC has small moist biases (~5–10%), which are within the uncertainty of radiosonde measurements, in thin and high clouds. Temperature biases of all data are within ±1 K at altitudes above the 700 hPa level but increase with decreasing altitude. Cloud‐cleared retrievals lead to large AIRS cold biases (reaching about −2 K) in the lower troposphere for large COD, enhancing the cold biases inherited from the NNW. Consequently, AIRS GOOD quality T root‐mean‐squared errors (RMSEs) are slightly smaller than the NNW errors in thin clouds (1.5–2.5 K) but slightly larger than the NNW errors for thick COD (reaching 3.5 K near the surface). The AIRS BEST quality control retains retrievals with uncertainties closer to those of the NNW. The AIRS error estimates reported in the L2 product tend to underestimate the precision (RMSE) implied by comparisons to the radiosonde measurements and do not reflect the observed cloud dependency of uncertainties.