CFSR Reanalysis Research Articles

Comparative analysis of satellite and reanalysis data with ground‐based observations in Northern Ghana

AbstractAccurate predictions of streamflow and flood events are contingent upon the availability of reliable hydrometeorological data. In regions characterized by scarcity of ground‐based hydrometeorological observations, satellite and reanalysis data assume prominence as alternative predictors. Floods and droughts have emerged as a significant concern in Northern Ghana, yet the scarcity of ground‐based hydrometeorological data impedes effective prediction of these hydrological events. Consequently, the identification of suitable surrogate hydrometeorological data holds paramount importance in addressing these challenges. This study, therefore, assessed the accuracy of satellite and reanalysis data against ground‐based data in Northern Ghana. Rainfall and mean temperature spanning from 1998 to 2019 and soil moisture datasets from 2019 to 2022 were collected from GMet, ISMN (ground‐based), CHIRPS, PERSIANN‐CDR, ERA5, ARC2, MERRA‐2, TRMM and CFSR (satellite and reanalysis). Employing rigorous statistical measures, namely standard deviation, mean absolute error (MAE) and mean bias error (MBE), the accuracy of these datasets was thoroughly evaluated. The results revealed that CHIRPS and PERSIANN‐CDR exhibited superior accuracy in rainfall simulation, with CHIRPS demonstrating particularly consistent congruence with observed data. In terms of mean temperature prediction, ERA5 surpassed MERRA‐2 and CFSR. Regarding soil moisture assessments, both ERA5 and CFSR offered satisfactory simulations. Hence, our findings advocate for the preference of CHIRPS (for rainfall data), ERA5 (for temperature data) and a combination of CFSR/ERA5 (for soil moisture data) as dependable primary data sources for streamflow modelling, drought analysis, flood prediction and water resource management in the context of Northern Ghana.

Contribution to the Study of Forest Fires in Semi-Arid Regions with the Use of Canadian Fire Weather Index Application in Greece

Forest fires are of critical importance in the Mediterranean region. Fire weather indices are meteorological indices that produce information about the impact as well as the characteristics of a fire event in an ecosystem and have been developed for that reason. This study explores the spatiotemporal patterns of the FWI system within a study area defined by the boundaries of the Greek state. The FWI has been calculated and studied for current and future periods using data from the CFSR reanalysis model from the National Centers for Environmental Protection (NCEP) as well as data from NASA satellite programs and the European Commission for Medium-Range Weather Forecasts (ECWMF) in the form of netCDF files. The calculation and processing of the results were conducted in the Python programming language, and additional drought- and fire-related indices were calculated, such as the standardized precipitation index (SPI), number of consecutive 50-day dry periods (Dry50), the Fosberg fire weather index (FFWI), the days where the FWI exceeds values of 40 and 50 days (FWI > 40) and (days FWI > 50). Similar patterns can easily be noted for all indices that seem to have their higher values concentrated in the southeast of the country owing to the higher temperatures and more frequent drought events that affect the indices’ behavior in both the current and future periods.

Diagnosis and modelling of two destructive derecho events in European Russia in the summer of 2010

In the summer of 2010, European Russia experienced an unprecedented high air temperature, catastrophic wildfires, heavy air pollution, but also several severe convective storms. Two of them occurring on June 27 and July 29 had a particularly high intensity and resulted in unprecedented forest damage. In this study, we estimate storm-related forest damage using Landsat images and analyse the synoptic-scale and mesoscale peculiarities of these storms formation and evolution using satellite and reanalyses data, surface observations, and mesoscale numerical simulation. The total area of stand-replacing windthrow caused by these two storms amounts to 1250 km2. We found for both events that dark-coniferous and pine forests were more impacted than deciduous and re-grown forests. Damage track length exceeds 500 km for one storm and 600 km for another; therefore, we classify them as derecho events, which are documented for the first time in Russia. Formation of both derecho-producing mesoscale convective systems (MCSs) was triggered by fast-moving waves formed on the polar front under conditions of substantial temperature gradients on a western flank of long-lived blocking anticyclones in their termination dates. Backward trajectories analysis shows that advection from the Caspian Sea was one of the sources of high atmospheric water content during the MCSs formation. Both MCSs developed within environments of moderate to strong convective instability and extremely high wind shear due to strong midlevel jet stream. We performed convection-permitting simulation of storms using the WRF model started with the initial data of CFSR and ERA5 reanalysis. For the event of June 27, the simulations with both initial conditions underestimate the maximum wind gusts since the model reproduces only a squall line without dominating derecho-producing storm. For the event of July 29, the ERA5 based simulation successfully reproduced the main features of the storm life cycle, while the CFS-based simulation substantially underestimated the wind gusts and the storm-affected area. Our results highlight the importance of the atmospheric blocking influence on the convective severe storms formation and development, which deserves further research.

Using neural network to improve sea level prediction along the southeastern Brazilian coast

The objective of this study is to apply data-based models to improve sea level prediction along the southeastern Brazilian coast. Focus is placed on the atmospheric-forced low-frequency variability (∼0,3 to 0,03 day −1), which is associated primarily with propagation of Coastal Trapped Waves along the continental shelf, and possibly with local wind forcing. Process-based hydrodynamic models, that solve complex physical equations to simulate a dynamical system, have been traditionally used to predict sea level. These models, however, may present inaccuracies related to their physical formulation, numerical approximations, unresolved small scale processes, model parameterizations, or initial and boundary conditions. A combined approach, using a neural network to update the outputs of a hydrodynamic model (Hycom) in order to correct the errors, allows for the integration of all available knowledge to generate more reliable predictions. Time series of coastal sea level at three stations (Cananéia, Ilha Fiscal and Imbituba) and atmospheric data from the CFSR reanalysis were used to train the data-based model. A novel methodology to preprocess the input data is presented, and it was critical to the reduction of predictions errors. Correlation maps between coastal sea level and the atmospheric forcing were calculated, and the areas of highest correlation were defined. While a purely data-based model is able to match the predictions of the hydrodynamic model for these stations, the combined approach goes a step further, reducing the errors in ∼50% for the frequency band of the atmospherically forced sea level variability.

The importance of the sea ice marginal zone for the surface turbulent heat fluxes in Arctic on the basis of NCEP CFSR reanalysis

The importance of the sea ice marginal zone for the surface turbulent heat fluxes in Arctic on the basis of NCEP CFSR reanalysis

SWRL Net: A Spectral, Residual Deep Learning Model for Improving Short-Term Wave Forecasts

AbstractSkillful nearshore wave forecasts are critical for providing timely alerts of hazardous wave events that impact navigation or recreational beach use. While typical forecasts provide bulk wave parameters (wave height and period), spectral details are needed to correctly predict wave and associated circulation dynamics in the nearshore region. Currently, global wave models, such as WAVEWATCH III (WW3), make spectral predictions, but do not assimilate regional buoy observations. Here, Spectral Wave Residual Learning Network (SWRL Net), a fully convolutional neural network, is trained to take recent WW3 forecasts and buoy observations, and produce corrections to frequency-directional WW3 spectra, transformed into directional buoy moments, for up to 24 h in the future. SWRL Net is trained with 10 years of collocated NOAA’s WW3 CFSR reanalysis predictions and buoy observations at three locations offshore of the U.S. western coast. At buoy locations SWRL Net residual corrections result in wave height root-mean-square error (RMSE) reductions of 23%–50% in the first 6 h and 10%–20% thereafter. Sea frequencies (5–10 s) show the most improvement compared to swell (12–20 s). SWRL Net reduces mean direction RMSE by 28%–54% and mean period RMSE by 20%–56% over 24 forecast hours. While each model is trained and tested at independent locations, SWRL Net exhibits generalization when introduced to data from other locations, suggesting future development may be composed of training sets from multiple locations.

Validation of global wind wave hindcasts using ERA5, MERRA2, ERA-Interim and CFSRv2 reanalyzes

Four global wind wave hindcasts based on ERA5, MERRA2, ERA-I and CFSR reanalyses and spectral wave model WAVEWATCH III for the period from 1980 to 2017 have been validated against satellite altimetry and NDBC buoys for 1 year. Hindcast based on newly released ECMWF reanalysis ERA5 demonstrated the best agreement with both satellite altimetery (with normalized bias being up to 5%, and RMSE up to 0.5 m) and buoy measurements, including values of upper percentiles. In general, all hindcasts show good correspondence with the observational data and thus can be used in further wind wave climate studies.

First Satellite Measurements of Carbon Dioxide in the Earth’s Atmosphere (From the SI-1 Spectrometer Aboard the Meteor Satellite in 1979)

Spectral measurements of the outgoing thermal IR radiation carried out in 1977 and 1979 (SI-1 instrument, Meteor satellite) are used for estimating the CO2 content in the Earth’s atmosphere. A special algorithm based on the technique of artificial neural networks has been developed for solving the inverse problem of determining the CO2 content. The algorithm uses experimental spectra, as well as data on temperature and humidity from the NCEP CFSR reanalysis archive. Despite the presence of a noticeable variation in individual values of CO2 content in 1977 and 1979 (330–350 ppm), the estimates clearly demonstrate the seasonal variations of the carbon dioxide content for Europe and low values for the Southern Hemisphere. The spatial and temporal averaging of the retrieved CO2 estimates gives mean winter and spring–summer values for the European region at 338.2 and 336.5 ± 3.1 ppmv, correspondingly, which is in a good agreement with the ground-based measurements at Mauna Loa station.

The Tropical Easterly Jet over Africa, its representation in six reanalysis products, and its association with Sahel rainfall

AbstractThis paper compares the characteristics of the Tropical Easterly Jet (TEJ) and upper‐level winds in six reanalysis products, compares them with soundings at seven West African locations, examines the relationship between Sahel rainfall and the TEJ, and examines factors influencing the TEJ. The jet characteristics assessed by MERRA2, NCEP 1, JRA 55, and ERA 5 are similar. CFSR and 20th Century Reanalysis are outliers in nearly every analysis, overestimating wind speeds by as much as 25 to 40% compared to other reanalyses. Over the period 1948 to 2014, the correlation between rainfall and TEJ magnitude is .72. Arguments based on observations and modelling studies provide evidence that on interannual scales changes in the TEJ are not forced by rainfall, that large‐scale factors drive the TEJ. Potential mechanisms are discussed for a causal relationship such that a strong jet leads to high rainfall. However, further modelling efforts are needed to conclusively determine whether the TEJ/Sahel rainfall link is a result of common forcing factors. The factors that appear to control jet strength include sea‐surface temperature (SST) contrast between the central equatorial Pacific and central equatorial Indian Ocean (correlation of −.64), SST contrast between the central equatorial and the southern subtropical Indian Ocean (correlation of −.39), the latitude of the shift between upper‐tropospheric easterlies and westerlies in the Southern Hemisphere (correlation of −.84 at 150 hPa), and the intensity of the Southern Hemisphere westerlies (correlation of +.52 at 200 hPa). This suggests considerable control on the TEJ by extra‐tropical circulation in the Southern Hemisphere.

Tide-Surge Interaction in the Pearl River Estuary: A Case Study of Typhoon Hato

In this study, the characteristics and mechanisms of tide-surge interaction in the Pearl River Estuary (PRE) during Typhoon Hato in August 2017 are studied in detail using a 3D nearshore hydrodynamic model. The wind field of Typhoon Hato is firstly reconstructed by merging the Holland parametric tropical cyclone model results with the CFSR reanalysis data, which enables the model to reproduce the pure astronomical tides and storm tides well; especially the distinctive oscillation pattern in the measured water levels due to the passage of the typhoon has been captured. Three different types of model runs are conducted in order to separate the water level variations due to the astronomical tide, storm surge and tide-surge interactions in the Pearl River Estuary. Results show the strong tidal modulation of the surge level, as well as alteration of the phase of surge which also changes the peak storm tidal level, in addition to the tidal modulation effects. In order to numerically assess the contributions of three nonlinear processes in the tide-surge interaction and quantify their relative significance, the widely used ``subtraction" approach and a new “addition" approach are tested in this study. The widely used “subtraction'' approach is found unsuitable for the assessment due to the ``rebalance" effect and thus a new “addition'' approach is proposed along with a new indicator to represent the tide-surge interaction, from which more reasonable results are obtained. Detailed analysis using the ``addition'' approach indicates that the quadratic bottom friction, shallow water effect and nonlinear advective effect play the first, second and third most important roles in the tidal-surge interaction in the estuary, respectively.

Sub-monthly evolution of the Caribbean Low-Level Jet and its relationship with regional precipitation and atmospheric circulation

The summer spatial structure and sub-monthly temporal evolution of one of the key dynamical features of Central American climate, the Caribbean Low-Level Jet (CLLJ), is investigated by means of extended empirical orthogonal functions (EEOFs). The Caribbean 925-hPa zonal wind from the CFSR reanalysis for the period 1979 – 2010 is used for the analysis. This approach reveals new insights into the dynamical processes and spatio-temporal evolution of the CLLJ summer intensification, and through lead and lag linear regressions, significant climate links in the broader Caribbean region are identified. The results show that the CLLJ generates significant precipitation and temperature responses with a distinct temporal evolution over the Caribbean-Atlantic domain to that over the tropical Pacific, which hints at different underlying controlling mechanisms over these two large-scale regions. These anomalies are linked with the mid and upper tropospheric circulation, where a vertical cell over the Caribbean (ascending at the jet exit and subsiding at its entrance) varies in phase with large-scale divergence over the Pacific Ocean. Extratropical hemispheric-wide waves and the weakening of a thermal low in northeast Mexico-central US are identified as potential triggering factors for the CLLJ summer intensification. Two leading modes of tropical variability, El Niño Southern Oscillation and the Madden-Julian Oscillation, are found to modulate the CLLJ by intensifying it and prolonging its life cycle. Details of the underlying mechanisms are provided. These results help to advance the understanding of the processes that modulate local climate variations, which is an important issue in view of the rapid climate change the region is undergoing.

Relationships between Sea Ice Concentration and Wind Speed over the Arctic Ocean during 1979–2015

AbstractNCEP CFSR reanalysis 6-hourly fields from 1979 to 2015 were used to investigate the relationships of sea ice concentration (SIC), atmospheric stratification, surface roughness, and wind speed at 10-m height (W10) and 850-hPa level (W850). We found that in autumn (September–November), winter (December–February), and spring (March–May) a lower SIC favors less-stable stratification and a higher W10. In autumn, the decrease in SIC is strongest, and SIC has its strongest correlation with the atmospheric stratification, W10, and the ratio of W10 and W850 (WSR). W10 and WSR have increased in autumn, and the negative trends in SIC typically are collocated with positive trends in W10 and WSR. In winter, W850 has negative trends over the Arctic Ocean, which, together with the lack of decrease of SIC in the central Arctic, has prevented W10 from increasing in winter. The winter trends are notably different from those for autumn, but the correlations are fairly similar. In autumn, winter, and spring, the negative correlation between SIC and W10 originated from the reduction of both stratification and aerodynamic surface roughness z0 with a reduction of SIC. The dependence of z0 on SIC is, however, weak in NCEP CFSR. In summer, the ratio of W10 and W850 has increased over large areas. The correlations between SIC and atmospheric variables were stronger on interannual time scales than on subseasonal time scales. The causal relationships are complicated by the two-way interaction between SIC and W10. In most cases, especially in summer, SIC increases after periods of W10 exceeding 5 m s−1.

Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries

AbstractLong‐term hourly time series representing the photovoltaic (PV) generation in European countries have been obtained and made available under open license. For every country, four different PV configurations, ie, rooftop, optimum tilt, tracking, and delta have been investigated. These are shown to have a strong influence on the hourly difference between electricity demand and PV generation. To obtain PV time series, irradiance from CFSR reanalysis dataset is converted into electricity generation and aggregated at country level. Prior to conversion, reanalysis irradiance is bias corrected using satellite‐based SARAH dataset and a globally applicable methodology. Moreover, a novel procedure is proposed to infer the orientation and inclination angles representative for PV panels based on the historical PV output throughout the days around summer and winter solstices. A key strength of the methodology is that it does not rely on historical PV output data. Consequently, it can be applied in places with no existing knowledge of PV performance.

Possible causes for the asymmetric evolution between the aerosol optical depth over East Asia and eastern United States during boreal spring

Observations have suggested that the 550‐nm aerosol optical depth (AOD) decreases over East Asia (EA) but increases over eastern United States (EUS) in boreal spring. Based on the NCEP CFSR reanalysis and TRMM 3A25 data set, the present work indicates that this asymmetry can be ascribed to the distinct feedback of general circulation and atmospheric heat source (AHS) between the two regions. In April and May, condensation heating becomes dominant in the AHS over EA and leads to low‐level southerly and upper‐tropospheric northerly flow in response. Subsequently, deep convection with strong ascending motion is generated, which reduces AOD over EA via the effect of vertical ventilation and rainfall‐related washout. Meanwhile, the AHS over EUS is induced by circulation originating from the Tropics and mid–high latitudes. Thus, AOD further increases over EUS due to the absence of vertical ventilation and the import of dust aerosols from EA in the upper troposphere during late spring. Also discussed is the role played by the Tibetan Plateau in this distinct AHS–circulation relationship between EA and EUS.

Impacts of Atmospheric Rivers on Precipitation in Southern South America

AbstractThis study quantifies the impact of atmospheric rivers (ARs) on precipitation in southern South America. An AR detection algorithm was developed based on integrated water vapor transport (IVT) from 6-hourly CFSR reanalysis data over a 16-yr period (2001–16). AR landfalls were linked to precipitation using a comprehensive observing network that spanned large variations in terrain along and across the Andes from 27° to 55°S, including some sites with hourly data. Along the Pacific (west) coast, AR landfalls are most frequent between 38° and 50°S, averaging 35–40 days yr−1. This decreases rapidly to the south and north of this maximum, as well as to the east of the Andes. Landfalling ARs are more frequent in winter/spring (summer/fall) to the north (south) of ~43°S. ARs contribute 45%–60% of the annual precipitation in subtropical Chile (37°–32°S) and 40%–55% along the midlatitude west coast (37°–47°S). These values significantly exceed those in western North America, likely due to the Andes being taller. In subtropical and midlatitude regions, roughly half of all events with top-quartile precipitation rates occur under AR conditions. Median daily and hourly precipitation in ARs is 2–3 times that of other storms. The results of this study extend knowledge of the key roles of ARs on precipitation, weather, and climate in the South American region. They enable comparisons with other areas globally, provide context for specific events, and support local nowcasting and forecasting.

Definition of new summer monsoon index for Vietnam region

In this study, we define a new summer monsoon index for Vietnam region (VSMI) by averaging 850hPa zonal wind (U850hPa) over the area of 5-15oN and 100-110oE using the 1981-2010 CFSR reanalysis data. The evaluation results show that VSMI index can reflect well the interannual variability of both low and high broad-scale circulation of the summer monsoon. On the intraseasonal timescale, the pentad-to-pentad VSMI index has a close relationship to the local summer monsoon rainfall observed. The correlation coefficients between VSMI index and rainfall are at 95% confidence level and show clearly effects of summer monsoon on the local rainfall due to terrain. The comparison results among three indices show that the VSMI index is the most suitable index in the term of describing summer monsoon for Vietnam region. In addition, features of summer monsoon as intensity, onset and withdrawal date as well as break event can be defined by the VSMI index.

Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling

CO2 remains the greenhouse gas that contributes most to anthropogenic global warming, and the evaluation of its emissions is of major interest to both research and regulatory purposes. Emission inventories generally provide quite reliable estimates of CO2 emissions. However, because of intrinsic uncertainties associated with these estimates, it is of great importance to validate emission inventories against independent estimates. This paper describes an integrated approach combining aircraft measurements and a puff dispersion modelling framework by considering a CO2 industrial point source, located in Biganos, France. CO2 density measurements were obtained by applying the mass balance method, while CO2 emission estimates were derived by implementing the CALMET/CALPUFF model chain. For the latter, three meteorological initializations were used: (i) WRF-modelled outputs initialized by ECMWF reanalyses; (ii) WRF-modelled outputs initialized by CFSR reanalyses and (iii) local in situ observations. Governmental inventorial data were used as reference for all applications. The strengths and weaknesses of the different approaches and how they affect emission estimation uncertainty were investigated. The mass balance based on aircraft measurements was quite succesful in capturing the point source emission strength (at worst with a 16% bias), while the accuracy of the dispersion modelling, markedly when using ECMWF initialization through the WRF model, was only slightly lower (estimation with an 18% bias). The analysis will help in highlighting some methodological best practices that can be used as guidelines for future experiments.

Effect of climate change on wind waves generated by anticyclonic cold front intrusions in the Gulf of Mexico

Anticyclonic cold surges entering the Gulf of Mexico (Nortes) generate ocean waves that disrupt maritime activities. Norte derived waves are less energetic than the devastating waves from tropical cyclones, but more frequent (~ 22 events/year) and with larger spatial influence. Despite their importance, few studies characterize Nortes derived waves and assess the effects of climate change on their occurrence. This study presents a method to identify and characterize Nortes with relation to their derived waves in the Gulf of Mexico. We based the identification of Nortes on synoptic measurements of pressure differences between Yucatan and Texas and wind speed at different buoy locations in the Gulf of Mexico. Subsequently, we identified the events in the CFSR reanalysis (present climate) and the CNRM-M5 model for the present climate and the RCP 8.5 scenario. We then forced a wave model to characterize the wave power generated by each event, followed by a principal component analysis and classification by k-means clustering analysis. Five different Nortes types were identified, each one representing a characteristic intensity and area of influence of the Norte driven waves. Finally, we estimated the occurrence of each Norte type for the present and future climates, where the CNRM-M5 results indicate that the high-intensity events will be less frequent in a warming climate, while mild events will become more frequent. The consequences of such changes may provide relief for maritime and coastal operations because of reduced downtimes. This result is particularly relevant for the operational design of coastal and marine facilities.

Comparing SPI and RDI Applied at Local Scale as Influenced by Climate

Drought and wetness events were studied in the Northeast Algeria with SPI and RDI. The study area includes a variety of climatic conditions, ranging from humid in the North, close to the Mediterranean Sea, to arid in the South, near the Sahara Desert. SPI only uses precipitation data while RDI uses a ratio between precipitation and potential evapotranspiration (PET). The latter was computed with the Thornthwaite equation, thus using temperature data only. Monthly precipitation data were obtained from 123 rainfall stations and monthly temperature data were obtained from CFSR reanalysis gridded temperature data. Both data sets cover the period 1979–80 to 2013–14. Using ordinary kriging, the gridded temperature data was interpolated to all the locations having precipitation data, thus providing to compute SPI and RDI with the same observed rainfall data for the 3-, 6- and 12-month time scales. SPI and RDI were therefore compared at station level and results and have shown that both indices revealed more sensitive to drought when applied in the semi-arid and arid zones. Differently, more wetness events were detected by RDI in the more humid locations. Comparing both indices, they show a coherent and similar behavior, however RDI shows smaller differences among climate zones and time-scales, which is an advantage relative to the SPI and is likely due to including PET in RDI.

Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales

The spatial and temporal variability of droughts were studied for the Northeast Algeria using SPI and RDI computed with monthly precipitation data from 123 rainfall stations and CFSR reanalysis monthly temperature data covering the period 1979–80 to 2013–14. The gridded temperature data was interpolated to all the locations having precipitation data, thus providing to compute SPI and RDI with the time scales of 3-, 6- and 12-month with the same observed rainfall data. Spatial and temporal patterns of droughts were obtained using Principal Component Analysis in S-Mode with Varimax rotation applied to both SPI and RDI. For all time scales of both indices, two principal components were retained identifying two sub-regions that are similar and coherent for all SPI and RDI time scales. Both components explained more than 70% and 74% of drought spatial variability of SPI and RDI, respectively. The identified sub-regions are similar and coherent for all SPI and RDI time scales. The Modified Mann-Kendall test was used to assess trends of the RPC scores, which have shown non-significant trends for decreasing drought occurrence and severity in both identified drought sub-regions and all time scales. Both indices have shown a coherent and similar behavior, however with RDI likely showing to identify more severe and moderate droughts in the southern and more arid sub-region which may be due to its ability to consider influences of global warming. Results for RDI are quite uniform relative to time scales and show smaller differences among the various climates when compared with SPI. Further assessments covering the NW and NE of Algeria using longer time series should be performed to better understand the behavior of both indices.