ECMWF Forecasts Research Articles

High Water Level Forecast Under the Effect of the Northeast Monsoon During Spring Tides

One of the manifests of air-sea interactions is the change in sea level due to meteorological forcing through wind stress and atmospheric pressure. When meteorological conditions conducive to water level increase coincide with high tides during spring tides, the sea level may rise higher than expected and pose a flood risk to coastal low-lying areas. In Hong Kong, specifically when the northeast monsoon coincides with the higher spring tides in late autumn and winter, and sometimes even compounded by the storm surge brought by late-season tropical cyclones (TCs), the result may be coastal flooding or sea inundation. Aiming at forecasting such sea level anomalies on the scale of hours and days with local tide gauges using a flexible and computationally efficient method, this study adapts a data-driven method based on empirical orthogonal functions (EOF) regression of non-uniformly lagged regional wind field from ECMWF Reanalysis v5 (ERA5) to capture the effects from synoptic weather evolution patterns, excluding the effect of TCs. Local atmospheric pressure and winds are also included in the predictors of the regression model. Verification results show good performance in general. Hindcast using ECMWF forecasts as input reveals that the reduction of mean absolute error (MAE) by adding the anomaly forecast to the existing predicted astronomical tide was as high as 30% in February on average over the whole range of water levels, as well as that compared against the Delft3D forecast in a strong northeast monsoon case. The EOF method generally outperformed the persistence method in forecasting water level anomaly for a lead time of more than 6 h. The performance was even better particularly for high water levels, making it suitable to serve as a forecast reference tool for providing high water level alerts to relevant emergency response agencies to tackle the risk of coastal inundation in non-TC situations and an estimate of the anomaly contribution from the northeast monsoon under its combined effect with TC. The model is capable of improving water level forecasts up to a week ahead, despite the general decreasing model performance with increasing lead time due to less accurate input from model forecasts at a longer range. Some cases show that the model successfully predicted both positive and negative anomalies with a magnitude similar to observations up to 5 to 7 days in advance.

Evaluating vector winds over eastern China in 2022 predicted by the CMA-MESO model and ECMWF forecast

Vector winds play a crucial role in weather and climate, as well as the effective utilization of wind energy resources. However, limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models. In this study, we treat vector winds as a whole by employing a vector field evaluation method, and evaluate the mesoscale model of the China Meteorological Administration (CMA-MESO) and ECMWF forecast, with reference to ERA5 reanalysis, in terms of multiple aspects of vector winds over eastern China in 2022. The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds. Both models overestimate the wind speed in East China, and CMA-MESO overestimates the wind speed to a greater extent. The forecasting skill of the vector wind field in both models decreases with increasing lead time. The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast. There is a significant negative correlation between the model vector wind forecasting skill and terrain height. This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.

Improving sub-seasonal extreme precipitation forecasts over China through a hybrid statistical-dynamical framework

Skillful and reliable sub-seasonal extreme precipitation forecasts are crucial for disaster prevention and mitigation. In this study, we introduce a hybrid statistical-dynamical framework to predict monthly maximum one-day precipitation (Rx1D) and monthly maximum five-day precipitation (Rx5D) over China from May to October. In the hybrid statistical-dynamical framework, the ECMWF forecasts of precipitation and boreal summer intraseasonal oscillation (BSISO) indices are used as predictors to establish calibration model and bridging models, separately. The calibration model and bridging models are then merged to generate probabilistic forecasts of Rx1D and Rx5D. Our results suggest that the bridging models show better performance in predicting Rx1D and Rx5D than calibration model in May, June, and July when the BSISO indices are used as predictors. The forecast skill of calibration model is higher compared to bridging models in August, September, and October. The BMA merged forecasts take advantage of both calibration model and bridging models, and can provide skilful and reliable forecasts for both Rx1D and Rx5D prediction. To have a more comprehensive assessment, we also evaluate the prediction skill of the occurrence of extreme precipitation events with exceedance probabilities of 50%, 20%, and 5% for both Rx1D and Rx5D. The Brier skill score of merged forecasts indicates that the hybrid statistical-dynamical framework can also provide skilful forecasts for the occurrence of extreme precipitation events greater than one-in-5-year return value of Rx1D (5Rx1D) and one-in-5-year return value of Rx5D (5Rx5D) in comparison to long-term climatology. These findings demonstrate the great potential of combining dynamical models and statistical models in improving sub-seasonal extreme precipitation forecasts.

Evaluation of precipitation temporal distribution pattern of post-processed sub-daily ECMWF forecasts

Evaluation of precipitation temporal distribution pattern of post-processed sub-daily ECMWF forecasts

A fast high resolution distributed hydrological model for forecasting, climate scenarios and digital twin applications using wflow_sbm

We investigated improvements to further speed up the multi-threaded scaling of the distributed hydrological model wflow_sbm. To gain insight in the speed improvements for operational applications, we connected the improved code to ECMWF’s Fields Database to allow for on-the-fly pre-processing of the forcing, which accelerated the entire forecasting chain. In the original wflow_sbm implementation, run times increased when more than eight threads were used due to Julia’s native threading overhead. Now, run times are 2 to 11 times faster, depending on the chosen routing scheme, number of threads and catchment size. We show the advantages of the improvements in a test setup where ECMWF forecasts and 35 years of ERA5 reanalysis data were used to force wflow_sbm models at 1x1 km spatial resolution for Europe. The attained speedup allows for using distributed hydrological models in large-scale hydrological forecasting and climate-change applications, which is currently often limited to lumped models.

Smoke with Induced Rotation and Lofting (SWIRL) Generated by the February 2009 Australian Black Saturday PyroCb Plume

AbstractThe discovery of smoke‐induced dynamical anomalies in the stratosphere associated with the 2019/2020 Australian New Year pyrocumulonimbus (pyroCb) super outbreak initiated a new field of study involving aerosol/weather anomalies. This paper documents the dynamical anomalies associated with the February 2009 Australian Black Saturday pyroCb outbreak. Positive potential vorticity anomalies (indicating anticyclonic rotation) with horizontal extent ∼1000 km and vertical thickness ∼2 km are associated with the plume, which we classify as a Smoke With Induced Rotation and Lofting (SWIRL). The SWIRL initially formed east of Australia, but then moved westward, crossing over Australia, and continuing to Africa. The SWIRL lasted for nearly three weeks (13 February–4 March), traveling ∼27,000 km and rising from potential temperatures of ∼410–500 K (altitudes ∼18–21 km). The altitude of the SWIRL is corroborated with coincident satellite‐based profiles of H2O, CO, HCN, O3, and aerosol extinction. A vertical temperature dipole (±3 K) accompanied the PV anomaly, as verified with coincident Global Navigation Satellite System radio occultation temperatures. The SWIRL dissipated as it passed over Africa. Operational ECMWF forecasts with early initialization (13 February) and late initialization (21 February) are examined. In the early case, the forecasted PV anomaly disappeared within 4 days, as expected due to lack of smoke heating in the forecast model. In the late case, while the forecasted PV anomaly was weaker than in the reanalyzes, a remnant anomaly remained out to 10 days.

Intrinsic predictability limits arising from Indian Ocean Madden–Julian oscillation (MJO) heating: effects on tropical and extratropical teleconnections

Abstract. Since the Madden–Julian oscillation (MJO) is a major source for tropical and extratropical variability on weekly to monthly timescales, the intrinsic predictability of its global teleconnections is of great interest. As the tropical diabatic heating associated with the MJO ultimately drives these teleconnections, the variability in heating among ensemble forecasts initialized from the same episode of the MJO will limit this predictability. In order to assess this limitation, a suite of 60 d ensemble reforecasts has been carried out with the ECMWF forecast model, spanning 13 starting dates from 1 November and 1 January for different years. The initial dates were chosen so that phases 2 and 3 of the MJO (with anomalous tropical heating in the Indian Ocean sector) were present in the observed initial conditions. The 51 members of an individual ensemble use identical initial conditions for the atmosphere and ocean. Stochastic perturbations to the tendencies produced by the atmospheric physics parameterizations are applied only over the Indian Ocean region (50–120∘ E). This guarantees that the spread between reforecasts within an ensemble is due to perturbations in heat sources only in the Indian Ocean sector. The point-wise spread in the intra-ensemble (or error) variance of vertically integrated tropical heating Q is larger than the average ensemble mean signal even at early forecast times; however the planetary wave (PW) component of Q (zonal waves 1–3) is predictable for 25 to 45 d, the time taken for the error variance to reach 50 % to 70 % of saturation. These scales never reach 90 % of saturation during the forecasts. The upper-level tropical PW divergence is even more predictable than Q (40 to 50 d). In contrast, the PW component of the 200 hPa Rossby wave source, which is responsible for propagating the influence of tropical heating to the extratropics, is only predictable for 20 to 30 d. A substantial ensemble spread of 300 hPa meridional wind propagates from the tropics to the Northern Hemisphere storm-track regions by days 15–16. Following the growth of upper-tropospheric spread in planetary wave heat flux, the stratosphere provides a feedback in enhancing the error via downward propagation towards the end of the reforecasts.

Correcting Subseasonal Forecast Errors with an Explainable ANN to Understand Misrepresented Sources of Predictability of European Summer Temperatures

Abstract Subseasonal forecasts are challenging for numerical weather prediction (NWP) and machine learning models alike. Forecasting 2-m temperature (t2m) with a lead time of 2 or more weeks requires a forward model to integrate multiple complex interactions, like oceanic and land surface conditions leading to predictable weather patterns. NWP models represent these interactions imperfectly, meaning that in certain conditions, errors accumulate and model predictability deviates from real predictability, often for poorly understood reasons. To advance that understanding, this paper corrects conditional errors in NWP forecasts with an artificial neural network (ANN). The ANN postprocesses ECMWF extended-range summer temperature forecasts by learning to correct the ECMWF-predicted probability that monthly t2m in western and central Europe exceeds the climatological median. Predictors are objectively selected from ECMWF forecasts themselves, and from states at initialization, i.e., the ERA5 reanalysis. The latter allows the ANN to account for sources of predictability that are biased in the NWP model itself. We attribute ANN corrections with two explainable artificial intelligence (AI) tools. This reveals that certain erroneous forecasts relate to tropical western Pacific Ocean sea surface temperatures at initialization. We conjecture that the atmospheric teleconnection following this source of predictability is imperfectly represented by the ECMWF model. Correcting the associated conditional errors with the ANN improves forecast skill. Significance Statement We want to understand occasions in which a numerical weather prediction (NWP) model fails to forecast a predictable event existing in the real world. For forecasts of European summer weather more than 2 weeks in advance, real predictable events are rare. When misrepresented by the model, predicted future states become needlessly biased. We diagnose these missed opportunities with an explainable neural network. The neural network is aware of the initial state and learns to correct the NWP forecast on occasions when it misrepresents a teleconnection from the western tropical Pacific Ocean to Europe. The explainable architecture can be useful for other applications in which conditional model errors need to be understood and corrected.

A performance evaluation of various physics schemes on the predictions of precipitation and temperature over the Tibet Autonomous Region of China

The WRF model has become an important tool for numerical weather prediction. However, the mechanism of different physics schemes in WRF for air temperature and precipitation predictions over the Tibet Autonomous Region of China is still uncertain. In this study, WRF experiments with distinct physics schemes are conducted on a rainfall event that occurred during the last two days of June 2021 in Tibet. The predictions of air temperature and precipitation from WRF are compared against observational data and the ECMWF forecast, and explained by the land-atmosphere interaction. Results confirm that WRF produces more accurate temperature and precipitation predictions than the widely used ECMWF forecast product. These analyses also reveal large differences in WRF performance in predicting the intensity and spatial patterns of rainfall and diurnal variations of temperature due to the use of different physics schemes. Among the selected physics schemes, the RRTMG scheme yields the highest accuracy forecasts for light rain to rainstorms. The other schemes give an ambiguous performance in relation to predicting various rainfall intensities. In addition, WRF applying the Noah-MP, Mellor-Yamada-Janjic, and RRTMG schemes contributes to alleviating the predicted cold air bias from the ECMWF and WRF control experiment, which is closely related to the surface net radiation overprediction, near-surface energy accumulation and the relatively accurate estimates of ground temperature. The RRTMG scheme shows the strongest capacity for predictions of diurnal variation of temperature, with the lowest RMSE of 3.18 °C and mean error of 2.05 °C. These values are 1.69 °C and 2.03 °C lower, respectively, compared to the ECMWF. The capacity of different schemes to relieve the mode's cold bias is ranked from high to low as RRTMG > Mellor-Yamada-Janjic > Grell 3D/Noah-MP.

Catchment‐scale skill assessment of seasonal precipitation forecasts across South Korea

AbstractClimate change is expected to make droughts more frequent and severe all over the world. South Korea is no exception and is already suffering from extreme droughts such as one that prolonged from 2013 to 2015 and caused nation‐wide damage. To mitigate drought damages, better management of existing water infrastructure is essential. A promising opportunity to improve operational decisions is to make use of seasonal weather forecasts that are provided by general circulation models and can be downscaled to the catchment scale. This study hence assesses the skill of seasonal forecasts over 20 catchments in South Korea, where the largest reservoirs are located. Datasets from four weather forecasting centres (ECMWF, UK Met Office, Météo France and DWD) have been evaluated over the period 2011–2020, and their skill quantified using the Continuous Ranked Probability Skill Score (CRPSS). We analyse how skill varies across the seasons and years, and if it can be linked to catchments characteristics. In doing so, we develop a methodology and a Python package to implement it, which is freely available for future applications to other regions. As for the study case, our results showed that among the four forecasting centres, ECMWF's forecasts were the most skilful in South Korea. In particular, seasonal forecasts outperform the climatology for 2 months of lead time and are more skilful during the wet season and in dry years. Linear bias correction is found to be useful to correct systematic seasonal biases, whereas we found no significant correlation between the catchment characteristics and forecast skill. We also investigated the possibility of anticipating dry years from seasonal forecasts and/or ENSO indices but found no significant link.

Subseasonal Ensemble Prediction of Flash Droughts over China

Abstract Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowledge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting. Significance Statement Flash droughts have raised considerable concern, but whether they can be predicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ratios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.

Correction method by introducing cloud cover forecast factor in model temperature forecast

Objective temperature forecast products can achieve better forecast quality by using one-dimensional regression correction directly based on the present model temperature forecast product, and the forecast accuracy can be further improved by adding appropriate auxiliary factors. In this paper, ECMWF forecast products and ground observation data from Fujian are used to revise the surface temperature at 2 m by introducing a cloud cover forecast factor based on the model temperature forecast correction method. Analysis shows that the forecast deviation of daily maximum and minimum temperature after the revision of a single-factor forecast is obviously correlated with cloud cover. A variety of prediction schemes are designed, and the final scheme is determined through comparative testing. The following conclusions are drawn: all schemes based on cloud cover grouping can improve forecast performance, and the total cloud cover scheme is generally better than the low cloud cover scheme. There is a good positive correlation between the forecast deviation of maximum temperature and the mean total cloud cover; that is, the more cloud cover, the bigger the deviation. The minimum temperature is negatively correlated with cloud cover when the cloud cover is less than 40% and positively correlated for the rest. The absolute forecast deviations of the maximum and minimum temperatures are larger when the total cloud cover is less. Whether for Tmax or Tmin forecast, the binary regression scheme after grouping consistently showed the best performance, with the lowest MAE. The final scheme was used to forecast the maximum and minimum temperature in 2021, and most verification indicators showed improvement in most forecast periods. The forecast accuracy for the 36-h daily maximum and minimum temperature is 81.312% and 91.480%, respectively, which is 2.4%–2.6% higher than the single-factor regression scheme. The forecast skill scores (FSS) reach 0.065 and 0.086, indicating that the method can effectively improve forecast quality in a stable manner and can be used for practical forecasting.

Ensemble forecast of an index of the Madden–Julian Oscillation using a stochastic weather generator based on circulation analogs

Abstract. The Madden–Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the tropical region. The MJO affects precipitation over highly populated areas, especially around southern India. Therefore, predicting its phase and intensity is important as it has a high societal impact. Indices of the MJO can be derived from the first principal components of zonal wind and outgoing longwave radiation (OLR) in the tropics (RMM1 and RMM2 indices). The amplitude and phase of the MJO are derived from those indices. Our goal is to forecast these two indices on a sub-seasonal timescale. This study aims to provide an ensemble forecast of MJO indices from analogs of the atmospheric circulation, computed from the geopotential at 500 hPa (Z500) by using a stochastic weather generator (SWG). We generate an ensemble of 100 members for the MJO amplitude for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using probabilistic scores and deterministic skill scores. According to score-based criteria, we find that a reasonable forecast of the MJO index could be achieved within 40 d lead times for the different seasons. We compare our SWG forecast with other forecasts of the MJO. The comparison shows that the SWG forecast has skill compared to ECMWF forecasts for lead times above 20 d and better skill compared to machine learning forecasts for small lead times.

Adapting to climate change through anticipatory action: The potential use of weather-based early warnings

As a crucially-needed adaptation to climate change, the United Nations plans to expand Early Warning Systems (EWS) for extreme weather to cover everyone on Earth. Given the growing interest in this climate change adaptation solution, we assess how well weather early warnings perform for extreme events in different parts of the world. First, we carry out a forecast verification for weather forecasts from the National Oceanic and Atmospheric Administration (NOAA) for 95th percentile extreme heat and extreme precipitation globally at 0.5° resolution, with three days of lead time. We present the results alongside similar verification results from ECMWF forecasts and a CHIRPS-GEFS forecast, to identify regions of the world with consistent forecast skill. We then overlay the skill of these short-term weather forecasts on top of climate change projections for the increasing frequency of the extreme events themselves. Based on these results, we offer policy implications for EWS investments in different regions. We find that in much of the tropics, weather forecasts have relatively poor skill in forecasting extreme temperature and precipitation events, calling for further investments in predictability. In the extra-tropics, most extreme heat and extreme precipitation events can be correctly forecasted, with better results for multi-day events and shorter lead-times. While there is room to improve predictability, end-to-end investments in EWS in these regions can focus on the use of existing skillful forecasts. Finally, most of the world's land area is projected to see an increase in the magnitude of extreme heat and precipitation events with climate change, and EWS investments in these regions should prepare for unprecedented extremes and changing vulnerabilities. These results provide a foundation for localized research on EWS in different parts of the world as well as evidence for policy and donors on how best to invest in EWS in different regions.

Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction

An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (WRF) is cycled and evaluated for western North Pacific (WNP) typhoons of year 2016. Conventional in situ data, radiance observations, and tropical cyclone (TC) minimum sea level pressure (SLP) are assimilated every 6 h using an 80-member ensemble. For all TC categories, the 6-h ensemble priors from the WRF/EnKF system have an appropriate amount of variance for TC tracks but have insufficient variance for TC intensity. The 6-h ensemble priors from the WRF/EnKF system tend to overestimate the intensity for weak storms but underestimate the intensity for strong storms. The 5-d deterministic forecasts launched from the ensemble mean analyses of WRF/EnKF are compared to the NCEP and ECMWF operational control forecasts. Results show that the WRF/EnKF forecasts generally have larger track errors than the NCEP and ECMWF forecasts for all TC categories because the regional simulation cannot represent the large-scale environment better than the global simulation. The WRF/EnKF forecasts produce smaller intensity errors and biases than the NCEP and ECMWF forecasts for typhoons, but the opposite is true for tropical storms and severe tropical storms. The 5-d ensemble forecasts from the WRF/EnKF system for seven typhoon cases show appropriate variance for TC track and intensity with short forecast lead times but have insufficient spread with long forecast lead times. The WRF/EnKF system provides better ensemble forecasts and higher predictability for TC intensity than the NCEP and ECMWF ensemble forecasts.

Extreme temperature events monitored by Raman lidar: Consistency and complementarity with spaceborne observations and modelling

AbstractTwo extreme temperature events (ETEs), both triggered by atmospheric blocking situations, were monitored by a pure rotational Raman lidar 20 km south of Paris. The first ETE lasted 6 days in late February 2018 with the onset of a cold wave surging from Siberia, and the second was a short heatwave that occurred at the end of July 2020. After calibration using simultaneous radiosoundings, lidar vertical profiles of temperature are derived with an uncertainty of less than 1 °C within both the planetary boundary layer and the lower/middle free troposphere (~3/6 km above ground level [a.g.l.] for day/night), with a final vertical resolution of 50 m and a temporal resolution of 30 min. Such capabilities fulfil the observational requirements of the World Meteorological Organisation and contribute to fill a gap left behind by current operational instruments, which struggle to follow the diurnal cycle of the planetary boundary layer due to insufficient spatial and temporal resolutions in the low troposphere. These case studies allow to assess how such weather events are represented by numerical weather prediction modelling and whether they can be correctly observed by the Infrared Atmospheric Sounding Interferometer (IASI) spaceborne mission. Our results demonstrate that during these events both ECMWF reanalyses and forecasts are in overall very good accordance with lidar observations (correlation >0.9 in average). Still, they show warm biases (~1°C–2°C) compared with the lidar and improvable temperature field representation in the boundary layer. The radiances measured by IASI, assimilated in the operational model, do not capture temperature profiles adequately below ~2–3 km a.g.l., mainly due to the altitude location of its weighting functions. The bias computed against the lidar reaches 2°C during the heatwave and 4°C during the cold wave case in the first 2 km of the atmosphere, beyond expected errors found in the literature.

Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation

Abstract. In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance.

Development of a hybrid wind field for modeling the tropical cyclone wave field

Due to the increasing trend of tropical cyclone occurrence in the recent decades, the necessity of developing models for forecasting and hindcasting these atmospheric-oceanic hazards, becomes more apparent. One of the uncertainties about the tropical cyclones is the wind field accuracy of modeling oceanic waves and flows. In this paper, six different wind fields have been examined for hindcasting the wave field of cyclone Gonu within the areas of the Arabian Sea and the Gulf of Oman. The wind field of ECMWF (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) data, that obtained via weather research forecasting (WRF) mesoscale modeling and three wind fields achieved via Holland, Rankin, Young and Sobey (S&Y) parametric models have been investigated as well. The wave field modeling results have been compared with the third-generation SWAN model in two stations within the area of Chabahar Bay. The wind field results obtained via the reanalysis models and WRF one indicate an appropriate estimation of the wave characteristics and the maximum wave height at the measurement stations. In order to better evaluate the results of different wind fields, the maximum wave height maps have been studied in the simulation time period. The results indicated that the maximum wave height values obtained from the reanalysis data do not match the track and development direction of the cyclone Gonu. The mismatch between storm development track and maximum wave height in WRF data can also be observed. The maximum significant wave height obtained via the parametric wind fields is very compatible with the cyclone Gonu track. Examining the maximum wave heights obtained via different wind fields, along with comparing the wave characteristics at the measurement stations, highlights the effect of different wind fields in hindcasting the wave characteristics, especially on the northern coasts of the Gulf of Oman. Examination of the maximum wave heights obtained from different wind fields has been investigated in this study along with comparison of wave characteristics at measurement stations. Results indicated that the optimal approach for simulating the tropical cyclone's wave field is to use a hybrid one. Therefore, a hybrid wind field has been developed which includes the wind characteristics of the parametric model in the areas near the storm source and the WRF meso scale model in the outer zone. The hybrid wind field has led to an improvement in the characteristics of waves within deep and coastal waters.

Prediction error growth in a more realistic atmospheric toy model with three spatiotemporal scales

Abstract. This article studies the growth of the prediction error over lead time in a schematic model of atmospheric transport. Inspired by the Lorenz (2005) system, we mimic an atmospheric variable in one dimension, which can be decomposed into three spatiotemporal scales. We identify parameter values that provide spatiotemporal scaling and chaotic behavior. Instead of exponential growth of the forecast error over time, we observe a more complex behavior. We test a power law and the quadratic hypothesis for the scale-dependent error growth. The power law is valid for the first days of the growth, and with an included saturation effect, we extend its validity to the entire period of growth. The theory explaining the parameters of the power law is confirmed. Although the quadratic hypothesis cannot be completely rejected and could serve as a first guess, the hypothesis's parameters are not theoretically justifiable in the model. In addition, we study the initial error growth for the ECMWF forecast system (500 hPa geopotential height) over the 1986 to 2011 period. For these data, it is impossible to assess which of the error growth descriptions is more appropriate, but the extended power law, which is theoretically substantiated and valid for the Lorenz system, provides an excellent fit to the average initial error growth of the ECMWF forecast system. Fitting the parameters, we conclude that there is an intrinsic limit of predictability after 22 d.

VERIFICATION OF DISTRICT-LEVEL WEATHER FORECAST OF KOLKATA AND ITS SUBURBS DURING MONSOON ‘2019 & 2020 FOR COMPARATIVE STUDY OF THE PERFORMANCE OF MODEL BETWEEN PRE COVID NON-LOCKDOWN AND COVID LOCKDOWN PERIOD

India Meteorological Department has started issuing district-level weather forecasts for up to 5 days on an operational basis from 1st June 2008. The weather parameters related to agro, namely rainfall, maximum and minimum temperature, wind speed, and direction, relative humidity, and cloudiness were chosen for outputs from the model. The rainfall forecast is generated based on multi-model ensemble techniques ( MME ) and ECMWF forecasts ( presently IMDGFS) are used for forecasting other parameters. These forecast generated for the districts of West Bengal by the model is further moderated by State Agro Met. Centre, Kolkata, and forwarded to six Agro Met. Field Units ( created by six agro-climatic zones in West Bengal ) and seven District Agro Met. Unit ( DAMU ) for preparation of weather-based District as well as Block level Agromet advisory bulletin which benefits the farmers in their crop production. Thus forecast verification of the model as well as moderated value for the monsoon season of 2019 and 2020 has been done to make a comparative study of the model performance concerning Kolkata and its suburbs based on Probability of Detection, False alarm, Heidke Skill score, Missing rate, Critical Success Index, True Skill Score, Hanssen, and Kuipers Index, etc. The monsoon rainfall of 2019 and 2020 was chosen to study the performance of the model concerning the pre-covid non-lockdown and covid lockdown period so that the effect of pollutants on the performance of the model can be analyzed. The verification results show that the model forecast, as well as a moderated forecast of this region, has to be more refined by taking inputs of other parameters and index that has been computed by different recent research works on this region because this region is under the influence of tropical climate. Moreover, the comparative study between monsoon 2019 and monsoon 2020 reveals that there have been changes in the performance of the model.