NCEP CFSv2 Research Articles

The First Operational Version of Taiwan Central Weather Bureau’s One-Tier Global Atmosphere–Ocean Coupled Forecast System for Seasonal Prediction

Abstract The first version of the Taiwan Central Weather Bureau one-tier (TCWB1T) fully coupled global atmospheric and oceanic modeling forecast system had been developed and implemented as a routine operation for seasonal prediction at Central Weather Bureau (CWB) in 2017, with a minor revision in 2020. Based on NCEP CFSv1, the global atmospheric model in NCEP CFSv1 was replaced by CWB’s atmospheric global spectral model (GSM) and coupled with the GFDL MOM3. Several parameters have been tested and tuned in the CWB atmospheric GSM, achieving an optimal configuration with better sea surface temperature (SST) predictions for integration more than one year. Using NCEP CFSR as the initial condition, TCWB1T conducted hindcasts from 1982 to 2011 and forecasts from 2012 to 2019 to analyze its performance. The results of these hindcasts and forecasts show that the TCWB1T can make useful predictions as verified against the observations of OISST, ERSST, CFSR, and GPCP based on the methods of EOF, RMSE, anomaly correlation, ranked probability skill score (RPSS), reliability diagram (RD), and relative operating characteristics (ROCs). TCWB1T also has the same level of skill scores as NCEP CFSv2 and/or the ECMWF fifth-generation seasonal forecast system (SEAS5), based on EOF, anomaly pattern correlation, climatological bias, RMSE, temporal correlation, and anomaly correlation percentage of forecast skill. TCWB1T shows forecast skill that is better in winter than in summer. Overall, it indicates that TCWB1T can be used for seasonal ENSO predictions.

Bias of ENSO-like SST breaks the connection between the North Atlantic SST and Northeast China spring precipitation in the NCEP CFSv2

Bias of ENSO-like SST breaks the connection between the North Atlantic SST and Northeast China spring precipitation in the NCEP CFSv2

Studying Conditions of Intense Harmful Algal Blooms Based on Long-Term Satellite Data

Harmful algal blooms (HABs) adversely impact aquatic organisms, human health, and the marine economy. The need to understand the origins and mechanisms of HAB occurrence and development determines the relevance of the study of these phenomena, including using remote sensing methods and assets. Here we present the results of a comprehensive study of conditions and precursors of some intense HABs detected in the water areas near the island of Chiloe (Chile, 2016), near the Kamchatka Peninsula (Russia, 2020), near the island of Hokkaido (Japan, 2021), among others. The study involves statistical analysis of long-term satellite and model data arrays on significant parameters of the marine environment and near-surface atmosphere, as well as empirical modeling of HAB risks. Information products on the following environmental parameters were used: sea surface temperature (SST, NOAA OISST, since 1981), the level of photosynthetically active radiation (PAR) and chlorophyll-a concentration (MODIS Ocean Color SMI, since 2000), sea surface salinity and height (HYCOM, since 1993), and near-surface wind speed and direction (NCEP CFSv2, since 1979). Quantitative assessments of the dynamics of informative criteria were applied. The key criterion is the ratio (Δσ) of the absolute deviation of the studied parameter from the expected norm to the RMS deviation of its values. Intense HABs were often preceded by excessive SST (up to Δσ ~1.99) and PAR (up to Δσ ~2.25) values, as well as low near-surface wind speed (up to Δσ ~−1.83). These environmental parameters considerably contribute to HAB generation and intensification. An approach and empirical function were proposed that allow us to assess the risk of HAB phenomena and reveal their precursors. Using the proposed approach and empirical function, the precursors of ten HABs were identified, nine of which were confirmed by in situ data. The results can be used as a tool for forecasting and studying the conditions for the occurrence of HABs, representing one of the promising directions for monitoring these dangerous phenomena.

Seasonal predictability of summer north african subtropical high in operational climate prediction models

Seasonal predictability of summer North African Subtropical High (NASH) is investigated in this study by utilizing the hindcast data from four operational climate prediction models, including BCC_CSM1.1(m), NCEP CFSv2, ECMWF System 4, and JMA CPSv2. By reconstructing indices describing the variations in intensity, area, eastern boundary and ridge line of the NASH, it is shown that the intensity and area indices present high prediction skills compared to the relatively low prediction skills of position indices. The multi-model ensemble (MME) mean, calculated as the arithmetic average of the four models, presents relatively higher and stabler skills than individual models. Further investigation indicates that the prediction skill of the NASH is largely reliant on the models’ ability in reproducing the relationship between the NASH indices and the tropical-to-subtropical sea surface temperature (SST) anomalies associated with the El Niño/Southern Oscillation (ENSO). The pattern of atmospheric circulation anomaly over the North Africa in response to ENSO is well captured by the models, which suggests the dominant source of predictability of the NASH.

Correcting Nonstationary Sea Surface Temperature Bias in NCEP CFSv2 Using Ensemble-Based Neural Networks

Abstract Sea surface temperature (SST) forecast products from the NCEP Climate Forecast System (CFSv2) that are widely used in climate research and prediction have nonstationary bias. In this study, we develop single- (ANN1) and three-hidden-layer (ANN3) neural networks and examine their ability to correct the SST bias in the NCEP CFSv2 extended seasonal forecast starting from July in the extratropical Northern Hemisphere. Our results show that the ensemble-based ANN1 and ANN3 can reduce the uncertainty associated with parameters assigned initially and dependence on random sampling. Overall, ANN1 reduces RMSE of the CFSv2 forecast SST substantially by 0.35°C (0.34°C) for the testing (training) data and ANN3 further reduces RMSE relatively by 0.49°C (0.47°C). Both the ensemble-based ANN1 and ANN3 can significantly reduce the spatially and temporally varying bias of the CFSv2 forecast SST in the Pacific and Atlantic Oceans, and ANN3 shows better agreement with the observation than that of ANN1 in some subregions. Significance Statement Global coupled climate models are the primary tool for climate simulation and prediction and provide initial and boundary conditions to drive regional climate models. SST is an essential climate variable simulated and forecast by global climate models, which suffers substantial biases both spatially and temporally. We apply the ensemble averaging of both single- and three-hidden-layer neural networks on the NCEP CFSv2 SST forecast. They can correct the identified SST error, though ANN3 performs relatively better than that of ANN1. Thus, ensemble-based ANN3 is a useful SST bias correction approach.

Seasonal Forecasting of Precipitation, Temperature, and Snow Mass over the Western United States by Combining Ensemble Postprocessing with Empirical Ocean–Atmosphere Teleconnections

Abstract Accurate and reliable seasonal forecasts are important for water and energy supply management. Recognizing the important role of snow water equivalent (SWE) for water management, here we include the seasonal forecast of SWE in addition to precipitation (P) and 2-m temperature (T2m) over hydrologically defined regions of the western United States. A two-stage process is applied to seasonal predictions from two models (NCEP CFSv2 and ECMWF SEAS5) through 1) postprocessing to remove biases in the mean, variance, and ensemble spread and 2) further reducing the residual errors by linear regression using climate indices. The adjusted forecasts from the two models are combined to form a superensemble using weights based on their prior skill. The adjusted forecasts are consistently improved over raw model forecasts probabilistically for all variables and deterministically for SWE forecasts. Overall skill of the superensemble usually improves upon the skill of forecasts from individual models; however, the percentage of seasons and regions with increased skill was approximately the same as those with decreased skill relative to the top performing postprocessed individual model. Seasonal SWE has the highest prediction skill, followed by T2m, with P showing lower prediction skill. Persistence contributes strongly to the skill of SWE and moderately to the skill of T2m. Furthermore, a distinct seasonality in the skill is seen in SWE, with a higher skill from late spring through early summer. Significance Statement Here we test the postprocessing of seasonal forecasts from two state-of-the-art seasonal prediction models for traditionally forecasted elements of precipitation and temperature as well as snowpack, which is important for water management. A two-stage procedure is utilized, including ocean and atmospheric teleconnection indices that have been shown to impact seasonal weather across the western United States. First, we adjust model output based on the average error in historic runs and then relate the remaining error to these teleconnection indices. A final step combines each adjusted model based on its historic performance. Forecasts are shown to improve upon the original models when assessed probabilistically. The snowpack forecasts perform better than temperature and precipitation forecasts with the best performance from late winter through early summer. Persistence is found to contribute strongly to the skill of snowpack and moderately to the skill of temperature.

Agricultural drought severity in NE Italy: Variability, bias, and future scenarios

This study investigated the variability of agricultural drought severity, as depicted by vegetation indices, and the bias in identifying drought events when considering a stationary vs nonstationary climate reference. The work leveraged gridded climate data (NCEP CFSv2, CHIRPS 1981–2022), soil properties (OpenLandMap), satellite imagery (Sentinel2/Landsat, 2000–2022), and future climate projections (NEX-GDDP, 2050) together with local knowledge of selected farms, to augment drought monitoring techniques and identify potential issues for agriculture. For the study domain, significant differences were observed when comparing drought characteristics using stationary and nonstationary drought indexes, with biases being not ubiquitous in either space or time of year. When developing sustainable drought mitigation and adaptation strategies, decision-makers should carefully address this uncertainty to avoid a possible underestimation of drought magnitude. Results showed a drought increase (∼50%) by the mid and late twenty-first century. Projection of future climate highlighted an even more significant impact (∼80%) with a wide variability of risk across the domain. As drought impact was also related to soil organic carbon (SOC), our results suggest that improving SOC content could be a sustainable strategy for enhancing soil drought resilience, especially in areas commonly characterized by low concentrations of organic carbon and nutrients. The analysis highlighted that drought impacts were also modulated by investment in irrigation infrastructure and irrigation efficiency. Researchers and land managers could apply the proposed analysis design to address historical, current and future indicators of vegetation conditions within irrigated regions. By providing spatio-temporal information on the patterns of drought impacts and their bias, this study supports identifying priority regions for targeted drought risk reduction and adaptation options, including water resources and soil management sustainability criteria, to move towards more resilient agricultural systems.

Atmospheric Ducts and Their Electromagnetic Propagation Characteristics in the Northwestern South China Sea

The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic weather stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ECMWF in studying atmospheric ducts using measured data, we analyzed the spatial–temporal distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. The study found that the NCEP CFSv2 data accurately capture the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height calculated from ERA5 and the measured data have good consistency. The occurrence rate and height of the evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns; conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layers, mainly concentrated from June to August. Monsoons play a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. In the end, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in various duct environments within a typical atmospheric duct structure.

A hybrid statistical-dynamical prediction model for summer precipitation in northwestern China based on NCEP CFSv2

As a typical arid/semi-arid area, summer rainfall in northwestern China (NWC) is critical for the ecological environment there. The NWC summer rainfall prediction using statistical and dynamical models shows very poor performance. The dynamical model NCEP CFSv2 shows very low prediction skill in NWC summer precipitation. NWC precipitation index (NWC PI) is defined as the regional mean summer precipitation over NWC. This study first investigates the soil moisture and SST (sea surface temperature) signals in the preceding months, and CFSv2 April predicted summer large-scale circulation anomalies significantly related with NWC PI during 1982–2016 (2011 missing). The April soil moisture in the upstream central Asia, and SST in the preceding winter over tropical Pacific, Atlantic and Indian Ocean are identified as predictor candidates in the observation. Besides, six April predicted large-scale circulation factors in CFSv2 are also hired. The potential model predictors are selected generally based on three criteria: where CFSv2 has significant prediction skill, and NWC PI is significantly correlated with the variable in both the observation and its counterpart in CFSv2 prediction. Hybrid models are then constructed based on different predictor combinations. The leave-one-out cross validation illustrates that the hybrid model using three predictors (April soil moisture in the upstream central Asia, April predicted zonal wind over the Maritime Continent, April predicted SLP over the Mediterranean Sea) presents best performance, with anomalies correlation coefficient–ACC of 0.65, percentage of the same sign–PERC 76.5%, relative error–RE 6.97%, and root mean square error–RMSE 0.344 mm/day for NWC PI. The April soil moisture in the upstream central Asia is linked with the summer water vapor flux over NWC, and April predicted zonal wind over the Maritime Continent is related with the large-scale circulation over NWC, while caution must be taken when April predicted SLP over the Mediterranean Sea is used as predictor of NWC PI.

Multi-Model Ensemble Prediction of Summer Precipitation in China Based on Machine Learning Algorithms

The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms: the decision tree (DT), random forest (RF), and adaptive boosting (AB) algorithms. On this basis, we explored the applicability of ML algorithms for ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy. Then, MME predictions based on optimal hyperparameters were constructed for different regions of China. The results showed that all three ML algorithms had an optimal maximum depth less than 2, which means that, based on the current amount of data, the three algorithms could only predict positive or negative precipitation anomalies, and extreme precipitation was hard to predict. The importance of each model in the ML-based MME was quantitatively evaluated. The results showed that NCEP CFSv2 and JMA CPS2 had a higher importance in MME for the eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provided a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME had a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019–2021, exhibiting a significant improvement over the other methods. This shows that ML methods have great potential for improving short-term climate prediction.

Precursory Signals in the Stratospheric Meridional Mass Circulation for Mid‐Latitude Cold Air Outbreak Events of High and Low Sub‐Seasonal Predictability

AbstractUsing the NCEP reforecast data (1999–2010) and NCEP's CFSv2 real‐time forecast data (2012–2019) from the S2S (Sub‐seasonal to Seasonal) project, we evaluated the sub‐seasonal skill by the CFSv2 model with respect to mid‐latitude cold air outbreak events (CAOs) in winter (November–February) and investigated the distinct features of the anomalies of surface temperature, stratospheric mass circulation, and wave activities between the top 11 “high‐skill” CAO events and the top 10 “low‐skill” events. Results show that the predictability limit tends to be longer for wide‐impact and long‐lasting CAOs occurred in Eurasia or both continents, but such tendency is not very robust. The model possesses a better sub‐seasonal skill when there is generally stronger and longer‐lasting equatorward cold air branch of isentropic meridional mass circulation, which is coupled with a stronger stratospheric poleward warm branch in the 3 weeks prior to the peak time of “high‐skill” CAOs. The largest contrast between “high‐skill” and “low‐skill” CAOs lies in the wavenumber‐1 component of stratospheric poleward warm branch. Both the amplitude in the middle stratosphere and vertically westward‐tilting of wavenumber‐1 waves in the lower stratosphere tend to be stronger (weaker) prior to “high‐skill” (“low‐skill”) CAOs, collaboratively contributing to the stronger (weaker) stratospheric poleward warm branch. The temporal evolution of intensity of stratospheric poleward warm branch, wave amplitude, and westward‐tilting are better captured by CFSv2 model at sub‐seasonal scale in the month before “high‐skill” CAOs. These results reveal the important role of stratosphere–troposphere connection in determining the windows of opportunity for skillful sub‐seasonal forecasts of CAOs.

A novel statistical-dynamical method for a seasonal forecast of particular matter in South Korea

Societal concerns about air quality in East Asia are still growing despite country-level efforts to reduce air pollution emissions. In coping with this growing concern, the government and the public demand a longer‑lead forecast of air quality to ensure sufficient response time until society prepares for countermeasures such as a temporary reduction of specific emission sources. Here we propose a novel method that produces skillful seasonal forecasting of wintertime (December to February) PM10 concentration over South Korea. The method is based on the idea that climate condition and air quality have co-variability in the seasonal time scales and that the state-of-art seasonal prediction model will benefit air quality forecasting. More specifically, a linear regression model is constructed to link observed winter PM10 concentration and climate variables where the predicted climate variables were furnished from NCEP CFSv2 forecast initialized during autumn. In this case, climate variables were selected as predictors of the model because they are not only physically related to air quality but also ‘predictable’ in CFS hindcast. Through analysis of retrospective forecasts of 20 winters for the period 2001–2020, we found this model shows statistically significant skill for the seasonal forecast of wintertime PM10 concentration.

Seasonal Prediction for May Rainfall over Southern China Based on the NCEP CFSv2

In this study, we assess the prediction for May rainfall over southern China (SC) by using the NCEP CFSv2 outputs. Results show that the CFSv2 is able to depict the climatology of May rainfall and associated circulations. However, the model has a poor skill in predicting interannual variation due to its poor performance in capturing related anomalous circulations. In observation, the above-normal SC rainfall is associated with two anomalous anticyclones over the western tropical Pacific and northeastern China, respectively, with a low-pressure convergence in between. In the CFSv2, however, the anomalous circulations exhibit the patterns in response to the El Niño-Southern Oscillation (ENSO), demonstrating that the model overestimates the relationship between May SC rainfall and the ENSO. Because of the onset of the South China Sea monsoon, the atmospheric circulation in May over SC is more complex, so the prediction for May SC rainfall is more challenging. In this study, we establish a dynamic-statistical forecast model for May SC rainfall based on the relationship between the interannual variation of rainfall and large-scale ocean-atmosphere variables in the CFSv2. The sea surface temperature anomalies (SSTAs) in the northeastern Pacific and the central-eastern equatorial Pacific, and the 500-hPa geopotential height anomalies over western Siberia in previous April, which exert great influence on the SC rainfall in May, are chosen as predictors. Furthermore, multiple linear regression is employed between the predictors obtained from the CFSv2 and observed May SC rainfall. Both cross validation and independent test show that the hybrid model significantly improve the model's skill in predicting the interannual variation of May SC rainfall by two months in advance.

A Hybrid Statistical-Dynamical Prediction Model for Summer Precipitation in Northwestern China Based on Ncep Cfsv2

A Hybrid Statistical-Dynamical Prediction Model for Summer Precipitation in Northwestern China Based on Ncep Cfsv2

Evaluation of subseasonal to seasonal forecasts over India for renewable energy applications

Abstract. This study evaluates subseasonal to seasonal scale (S2S) forecasts of meteorological variables relevant for the renewable energy (RE) sector of India from six ocean-atmosphere coupled models: ECMWF SEAS5, DWD GCFS 2.0, Météo-France's System 6, NCEP CFSv2, UKMO GloSea5 GC2-LI, and CMCC SPS3. The variables include 10 m wind speed, incoming solar radiation, 2 m temperature, and 2 m relative humidity because they are critical for estimating the supply and demand of renewable energy. The study is conducted over seven homogenous regions of India for 1994–2016. The target months are April and May when the electricity demand is the highest and June–September when the renewable resources outstrip the demand. The evaluation is done by comparing the forecasts at 1, 2, 3, 4, and 5-months lead-times with the ERA5 reanalysis spatially averaged over each region. The fair continuous ranked probability skill score (FCRPSS) is used to quantitatively assess the forecast skill. Results show that incoming surface solar radiation predictions are the best, while 2 m relative humidity is the worst. Overall SEAS5 is the best performing model for all variables, for all target months in all regions at all lead times while GCFS 2.0 performs the worst. Predictability is higher over the southern regions of the country compared to the north and north-eastern parts. Overall, the quality of the raw S2S forecasts from numerical models over India are not good. These forecasts require calibration for further skill improvement before being deployed for applications in the RE sector.

Contrasting Interannual Prediction between January and February Temperature in Southern China in the NCEP Climate Forecast System

AbstractThis study investigates the prediction of southern China surface air temperature (SAT) in January and February using hindcast and forecast dataset from the second version of the National Centers for Environmental Prediction Climate Forecast System, version 2 (NCEP CFSv2), for the period of 1983–2017. The observed January and February SAT in southern China is teleconnected with the Euro-Atlantic dipole (EAD) and the North Atlantic Oscillation (NAO), respectively. The February SAT is also teleconnected with El Niño–Southern Oscillation (ENSO) via the bridge with the Philippine Sea anticyclone. The CFSv2 better predicts southern China SAT in February than January, where the temporal correlation coefficients between the observed and predicted regional-mean SAT in February and January are +0.81 and +0.27 (+0.32 and +0.04), respectively, for the one-month (two month) ahead prediction. The better prediction in February coincides with 1) accurate responses of the Eurasian circulation and the Philippine Sea anticyclone to the NAO and the ENSO, respectively, and 2) a strong ENSO–NAO linkage. The poorer prediction in January is related to a stronger linkage of the predicted January SAT with the NAO rather than the EAD, as well as a weak ENSO–EAD linkage. These results advance our understanding of the subseasonal prediction of the winter temperature in southern China.

Subseasonal Deterministic Prediction Skill of Low-Level Geopotential Height Affecting Southern Africa

AbstractThe NCEP CFSv2 and ECMWF hindcasts are used to explore the deterministic subseasonal predictability of the 850-hPa circulation of a large domain over the Atlantic and Indian Oceans that is relevant to the weather and climate of the southern African region. For NCEP CFSv2, 12 years of hindcasts, starting on 1 January 1999 and initialized daily for four ensemble members up to 31 December 2010 are verified against ERA-Interim reanalysis data. For ECMWF, 20 years of hindcasts (1995–2014), initialized once a month for all the months of the year are employed in a parallel analysis to investigate the predictability of the 850-hPa circulation. The ensemble mean for 7-day moving averages is used to assess the prediction skill for all the start dates in each month of the year, with a focus on the start dates in each month that are representative of the week-3 and week-4 hindcasts. The correlation between the anomaly patterns over the study domain shows skill over persistence up into the week-3 hindcasts for some months. The spatial distribution of the correlation between the anomaly patterns show skill over persistence to notably reduce over the domain by week 3. A prominent area where prediction skill survives the longest, occur over central South America and the adjacent Atlantic Ocean.

ANALYSIS OF AN EXTRATROPICAL CYCLONE IN THE SOUTHWEST ATLANTIC: WRF MODEL BOUNDARY CONDITIONS SENSITIVITY

Abstract. Meteorological conditions characterize the southern Brazilian coast a cyclogenetic area. The current study seeks to analyse the sensitivity of the WRF model to initial and boundary meteorological conditions in the simulation of an extratropical cyclone that occurred on the southern Brazilian coast on October 28, 2018. For this purpose, the WRF model was set up for two experimental simulations using the NCEP FNL and the NCEP CFSv2 reanalysis data as initial/boundary conditions. The sensitivity analysis was carried out with the cyclone trajectory assessment and comparison with wind speed data from meteorological stations. The results show that the initial meteorological conditions significantly influence the simulation of the cyclone track. In a nutshell, the use of NCEP CFSv2 resulted in more accurate wind speed simulations when compared to the values observed in the stations. With correlation coefficient values around 0.7, and the lowest bias (−2.57 m/s) and RMSE (3.68 m/s). In contrast, using the NCEP FNL data, the lowest correlation coefficient and the highest bias and RMSE values were obtained: 0.58, −3.97 m/s and 4.91 m/s, respectively. However, both simulations tend to underestimate observational wind speed values. The superior performance of simulations using CFSv2 tends to be related to the finer horizontal resolution of this reanalysis data source.

Using the WRF Model to Refine NCEP CFSv2 Reanalysis Atmospheric Profile: A Southern Brazil Test Case

ABSTRACT. The vertical distribution of atmospheric parameters is pivotal to understand atmospheric dynamic processes. This study analyzes the use of the Weather Research and Forecasting (WRF) model to generate atmospheric profiles as an alternative for a local radiosonde. We performed simulations in the WRF model for two dates with distinct weather conditions (01/09/2018 – cloudy and 03/14/2018 – clear-sky). The simulated data were compared with radiosondes launched in the study area, both (i) along the entire profile and (ii) with focus on the Planetary Boundary Layer (PBL) – for the variables water vapor mixing ratio (q), potential temperature (θ) and wind speed (Speed). The results showed a high correlation between the simulated and observed profiles, with most R values higher than 0.9. Bias and RMSE, respectively, ranged between -1.29 – 0.66 g/kg and 0.48 – 2.01 g/kg for q; -0.52 – 0.25 K and 0.44 – 3.00 K for θ; and -0.20 – 1.31 m/s and 1.61 – 2.77 m/s for Speed. The difference between the profiles from different WRF nested grids was minimal, suggesting that a resolution of approximately 12 km is enough to a good trade-off between detailing and computational cost. WRF model seems suitable to simulate fine atmospheric profiles.Keywords: vertical profiles, numerical weather prediction, meteorology, remote sensing, planetary boundary layer. Uso do Modelo WRF para refinar perfis atmosféricos de reanálise NCEP CFSv2: um estudo de caso no Sul do BrasilRESUMO. A distribuição vertical dos parâmetros atmosféricos é fundamental para entender os processos dinâmicos da atmosfera. Este estudo analisa o uso do modelo Weather Research and Forecasting (WRF) na geração de perfis atmosféricos, como uma alternativa à radiossondagens locais. Foram realizadas simulações com o WRF para duas datas em condições de tempo distintas (09/01/2018 – alta nebulosidade e 14/03/2018 – céu claro). Os dados simulados foram comparados com radiossondagens lançadas na área de estudo, (i) ao longo de todo o perfil e (ii) com foco na camada limite planetária (PBL) – para as variáveis razão de mistura do vapor de água (q), temperatura potencial (θ) e velocidade do vento (Speed). Os resultados mostraram uma alta correlação entre os perfis simulados e observados, com a maioria dos valores de R superiores a 0,9. Viés e RMSE, respectivamente, variaram entre -1,29 – 0,66 g/kg e 0,48 – 2,01 g/kg para q; -0,52 – 0,25 K e 0,44 – 3,00 K para θ; e -0,20 – 1,31 m/s e 1,61 – 2,77 m/s para Speed. Mínimas diferenças entre os perfis das diferentes grades aninhadas sugerem que uma resolução horizontal de aproximadamente 12 km é um bom balanço entre detalhamento e custo computacional. O WRF mostrou potencial na simulação de perfis atmosféricos refinados.Palavras-chave: perfis verticais, previsão numérica do tempo, meteorologia, sensoriamento remoto, camada limite planetária.

The assessment of salinity impact on surface water mass transformation in the North Atlantic

In this study, we investigate sensitivity of total surface water transformation to salinity from different data sources. Here we use surface salinity from NCEP CFSv2 reanalysis, GLORYS ocean reanalysis, Aquarius satellite data and ISAS-15 (product of optimal interpolation of ARGO buoys) as well as heat and freshwater fluxes from NCEP CFSv2 reanalysis. The largest spread in estimates of salinity is observed for Aquarius satellite in subpolar regions. The best correspondence between transformation computed using salinity from different datasets is observed for subtropical waters. Model-based products (CFSv2 and GLORYS) are the most consistent in estimates of surface water transformation and the largest discrepancies between these datasets are observed in the regions with high freshwater fluxes.