scholarly journals Impacts of aerosol-radiation interaction on meteorological forecast over northern China by offline coupling the WRF-Chem simulated AOD into WRF: a case study during a heavy pollution event

2020 ◽  
Author(s):  
Yang Yang ◽  
Min Chen ◽  
Xiujuan Zhao ◽  
Dan Chen ◽  
Shuiyong Fan ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Weather Prediction ◽  
Northern China ◽  
Surface Radiation ◽  
Horizontal Wind ◽  
In Situ Observation ◽  
Near Surface ◽  
Pollution Event ◽  
Heavy Pollution

Abstract. To facilitate the future inclusion of aerosol-radiation interactions in the regional operational Numerical Weather Prediction (NWP) system – RMAPS-ST (adapted from Weather Research and Forecasting, WRF) at the Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), the impacts of aerosol-radiation interactions on the forecast of surface radiation and meteorological parameters during a heavy pollution event (December 6th–10th, 2015) over northern China were investigated. The aerosol information was simulated by RMAPS-Chem (adapted from WRF model coupled with Chemistry, WRF-Chem) and then offline-coupled into Rapid Radiative Transfer Model for General Circulation Models (RRTMG) radiation scheme of WRF to enable the aerosol-radiation feedback in the forecast. To ensure the accuracy of high-frequent (hourly) updated aerosol optical depth (AOD) field, the temporal variations of simulated AOD at 550 nm were evaluated against satellite and in-situ observations, which showed great consistency. Further comparison of PM2.5 with in-situ observation showed WRF-Chem reasonably captured the PM2.5 field in terms of spatial distribution and magnitude, with the correlation coefficients of 0.85, 0.89 and 0.76 at Beijing, Shijiazhuang and Tianjin, respectively. Forecasts with/without the hourly aerosol information were conducted further, and the differences of surface radiation, energy budget, and meteorological parameters were evaluated against surface and sounding observations. The offline-coupling simulation (with aerosol-radiation interaction active) showed a remarkable decrease of downward shortwave (SW) radiation reaching surface, thus helping to reduce the overestimated SW radiation during daytime. The simulated surface radiation budget was also improved, with the biases of net surface radiation decreased by 85.3 %, 50.0 %, 35.4 %, and 44.1 % during daytime at Beijing, Tianjin, Taiyuan and Jinan respectively, accompanied by the reduction of sensible (16.1 W m−2, 18.5 %) and latent (6.8 W m−2, 13.4 %) heat fluxes emitted by the surface at noon-time. In addition, the cooling of 2-m temperature (~ 0.40 °C) and the decrease of horizontal wind speed near surface (~ 0.08 m s−1) caused by the aerosol-radiation interaction over northern China helped to reduce the bias by ~ 73.9 % and ~ 7.8 % respectively, particularly during daytime. Further comparisons indicated that the simulation implemented AOD could better capture the vertical structure of atmospheric wind. Accompanied with the lower planetary boundary layer and the increased atmospheric stability, both U and V wind at 850 hPa showed the convergence which were unfavorable for pollutants dispersion. Since RMPAS-ST provides meteorological initial condition for RMPS-Chem, the changes of meteorology introduced by aerosol-radiation interaction would routinely impact the simulations of pollutants. These results demonstrated the profound influence of aerosol-radiation interactions on the improvement of predictive accuracy and the potential prospects to offline couple near-real-time aerosol information in regional RMAPS-ST NWP in northern China.

Impacts of aerosol-radiation interaction on meteorological forecast over northern China by offline coupling the WRF-Chem simulated AOD into WRF: a case study during a heavy pollution event

2020 ◽  
Author(s):  
Yang Yang ◽  
Dan Chen ◽  
Xiujuan Zhao
Keyword(s):  
Meteorological Parameters ◽  
Weather Prediction ◽  
Northern China ◽  
Surface Radiation ◽  
Horizontal Wind ◽  
In Situ Observation ◽  
Near Surface ◽  
Pollution Event ◽  
Heavy Pollution

<p>To facilitate the future inclusion of aerosol-radiation interactions in the regional operational Numerical Weather Prediction (NWP) system – RMAPS-ST (adapted from Weather Research and Forecasting, WRF) at Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), the impacts of aerosol-radiation interactions on the forecast of surface radiation and meteorological parameters during a heavy pollution event (December 6<sup>th</sup> -10<sup>th</sup>, 2015) over northern China were investigated. The aerosol information was simulated by RMAPS-Chem (adapted from WRF model coupled with Chemistry, WRF-Chem) and then offline-coupled into Rapid Radiative Transfer Model for General Circulation Models (RRTMG) radiation scheme of WRF to enable the aerosol-radiation feedback in the forecast. To ensure the accuracy of high-frequent (hourly) updated aerosol optical depth (AOD) field, the temporal variations of simulated AOD at 550nm were evaluated against satellite and in-situ observation, which showed great consistency. Further comparison of PM<sub>2.5</sub> with in-situ observation showed WRF-Chem reasonably captured the PM<sub>2.5</sub> field in terms of spatial distribution and magnitude, with the correlation coefficients of 0.85, 0.89 and 0.76 at Beijing, Shijiazhuang and Tianjin, respectively. Forecasts with/without the hourly aerosol information were conducted further, and the differences of surface radiation, energy budget, and meteorological parameters were evaluated against surface and sounding observations. The offline-coupling simulation (with aerosol-radiation interaction active) showed a remarkable decrease of downward shortwave (SW) radiation reaching surface, thus helps to reduce the overestimated SW radiation during daytime. The simulated surface radiation budget has also been improved, with the biases of net surface radiation decreased by 85.3%, 50.0%, 35.4%, and 44.1% during daytime at Beijing, Tianjin, Taiyuan and Jinan respectively, accompanied by the reduction of sensible (16.1 W m<sup>−2</sup>, 18.5%) and latent (6.8 W m<sup>−2</sup>, 13.4%) heat fluxes emitted by the surface at noon-time. In addition, the cooling of 2-m temperature (~0.40 °C) and the decrease of horizontal wind speed near surface (~0.08 m s<sup>-1</sup>) caused by aerosol-radiation interaction over northern China helped to reduce the bias by ~73.9% and ~7.8% respectively, particularly during daytime. Further comparison indicated that the simulation implemented AOD could better capture the vertical structure of atmospheric wind. Accompanied with the lower planetary boundary layer and the increased atmospheric stability, both U and V wind at 850hPa showed the convergence which were unfavorable for pollutants dispersion. Since RMPAS-ST provides meteorological initial condition for RMPS-Chem, the changes of meteorology introduced by aerosol-radiation interaction would routinely impact the simulations of pollutants. These results demonstrated the profound influence of aerosol-radiation interactions on the improvement of predictive accuracy and the potential prospects to offline couple near-real-time aerosol information in regional RMAPS-ST NWP in northern China.</p>

scholarly journals Impacts of aerosol–radiation interaction on meteorological forecasts over northern China by offline coupling of the WRF-Chem-simulated aerosol optical depth into WRF: a case study during a heavy pollution event

2020 ◽  
Vol 20 (21) ◽  
pp. 12527-12547
Author(s):  
Yang Yang ◽  
Min Chen ◽  
Xiujuan Zhao ◽  
Dan Chen ◽  
Shuiyong Fan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Meteorological Parameters ◽  
Northern China ◽  
Satellite Observations ◽  
Surface Radiation ◽  
Pollution Event ◽  
Heavy Pollution

Abstract. To facilitate the future inclusion of aerosol–radiation interactions in the regional operational numerical weather prediction (NWP) system RMAPS-ST (adapted from Weather Research and Forecasting, WRF) at the Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), the impacts of aerosol–radiation interactions on the forecast of surface radiation and meteorological parameters during a heavy pollution event (6–10 December 2015) over northern China were investigated. The aerosol information was simulated by RMAPS-Chem (adapted from the WRF model coupled with Chemistry, WRF-Chem) and then offline-coupled into the Rapid Radiative Transfer Model for General Circulation Models (RRTMG) radiation scheme of WRF to enable the aerosol–radiation feedback in the forecast. To ensure the accuracy of the high-frequency (hourly) updated aerosol optical depth (AOD) field, the temporal and spatial variations of simulated AOD and aerosol extinction coefficient at 550 nm were evaluated against in situ and satellite observations. Comparisons with in situ and Moderate Resolution Imaging Spectroradiometer (MODIS), AErosol Robotic NETwork (AERONET), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite observations showed that the model could reproduce the spatial and vertical distribution as well as the temporal variation of the polluted episode. Further comparison of PM2.5 with in situ observation showed WRF-Chem reasonably captured the PM2.5 field in terms of spatial distribution and magnitude, with the correlation coefficients of 0.85, 0.89, 0.76, 0.92 and 0.77 in Beijing, Shijiazhuang, Tianjin, Hebei and Henan, respectively. Forecasts with and without the aerosol information were conducted further, and the differences of surface radiation, energy budget and meteorological parameters were evaluated against surface and sounding observations. The offline-coupling simulation (with aerosol–radiation interaction active) showed a remarkable decrease in downward shortwave (SW) radiation reaching the surface, thus helping to reduce the overestimated SW radiation during the daytime. The simulated surface radiation budget was also improved, with the biases of net surface radiation decreased by 85.3 %, 50.0 %, 35.4 % and 44.1 % during the daytime in Beijing, Tianjin, Taiyuan and Jinan respectively, accompanied by the reduction of sensible (16.1 W m−2, 18.5 %) and latent (6.8 W m−2, 13.4 %) heat fluxes emitted by the surface around noon. In addition, the cooling of 2 m temperature (∼0.40 ∘C) and the decrease in horizontal wind speed near the surface (∼0.08 m s−1) caused by the aerosol–radiation interaction over northern China helped to reduce the bias by ∼73.9 % and ∼7.8 % respectively, particularly during the daytime. Further comparisons indicated that the simulation-implemented AOD could better capture the vertical structure of atmospheric wind. Accompanied with the lower planetary boundary layer and the increased atmospheric stability, both U and V wind at 850 hPa showed convergences which were unfavorable for pollutant dispersion. Since RMPAS-ST provides meteorological initial conditions for RMAPS-Chem, the changes of meteorology introduced by aerosol–radiation interaction would routinely impact the simulations of pollutants. To verify the statistical significance of the results, we further conducted the 24 h forecasts for a longer period lasting 27 d (13 January–8 February 2017), with no AOD field (NoAero) and WRF-Chem-simulated hourly AOD fields (Aero) included, as well as a constant AOD value of 0.12 (ClimAero). The 1-month results were statistically significant and indicated that the mean RMSE of 2 m temperature (wind speed at 10 m) in Aero and ClimAero relative to NoAero was reduced by 4.0 % (1.9 %) and 1.2 % (1.6 %). More detailed evaluations and analysis will be addressed in a future article. These results demonstrated the influence of aerosol–radiation interactions on the improvement of predictive accuracy and the potential prospects to offline coupling of near-real-time aerosol information in regional RMAPS-ST NWP in northern China.

scholarly journals Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1872 ◽  
Author(s):  
Lu Yi ◽  
Wanchang Zhang ◽  
Xiangyang Li
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Eastern China ◽  
Hydrological Modelling ◽  
Heavy Rainfall Event ◽  
In Situ Observation ◽  
Rainfall Runoff ◽  
Study Case

To compare the effectivenesses of different precipitation datasets on hydrological modelling, five precipitation datasets derived from various approaches were used to simulate a two-week runoff process after a heavy rainfall event in the Wangjiaba (WJB) watershed, which covers an area of 30,000 km2 in eastern China. The five precipitation datasets contained one traditional in situ observation, two satellite products, and two predictions obtained from the Numerical Weather Prediction (NWP) models. They were the station observations collected from the China Meteorological Administration (CMA), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG), the merged data of the Climate Prediction Center Morphing (merged CMORPH), and the outputs of the Weather Research and Forecasting (WRF) model and the WRF four-dimensional variational (4D-Var) data assimilation system, respectively. Apart from the outlet discharge, the simulated soil moisture was also assessed via the Soil Moisture Active Passive (SMAP) product. These investigations suggested that (1) all the five precipitation datasets could yield reasonable simulations of the studied rainfall-runoff process. The Nash-Sutcliffe coefficients reached the highest value (0.658) with the in situ CMA precipitation and the lowest value (0.464) with the WRF-predicted precipitation. (2) The traditional in situ observation were still the most reliable precipitation data to simulate the study case, whereas the two NWP-predicted precipitation datasets performed the worst. Nevertheless, the NWP-predicted precipitation is irreplaceable in hydrological modelling because of its fine spatiotemporal resolutions and ability to forecast precipitation in the future. (3) Gauge correction and 4D-Var data assimilation had positive impacts on improving the accuracies of the merged CMORPH and the WRF 4D-Var prediction, respectively, but the effectiveness of the latter on the rainfall-runoff simulation was mainly weakened by the poor quality of the GPM IMERG used in the study case. This study provides a reference for the applications of different precipitation datasets, including in situ observations, remote sensing estimations and NWP simulations, in hydrological modelling.

scholarly journals Wintertime Subkilometer Numerical Forecasts of Near-Surface Variables in the Canadian Rocky Mountains

2015 ◽  
Vol 143 (2) ◽  
pp. 666-686 ◽  
Author(s):  
Vincent Vionnet ◽  
Stéphane Bélair ◽  
Claude Girard ◽  
André Plante
Keyword(s):  
Relative Humidity ◽  
High Altitude ◽  
Rocky Mountains ◽  
Weather Prediction ◽  
Slope Angle ◽  
Forecast Accuracy ◽  
Surface Radiation ◽  
Canadian Rocky Mountains ◽  
Grid Spacing ◽  
Near Surface

Abstract Numerical weather prediction (NWP) systems operational at many national centers are nowadays used at the kilometer scale. The next generation of NWP models will provide forecasts at the subkilometer scale. Large impacts are expected in mountainous terrain characterized by highly variable orography. This study investigates the ability of the Canadian NWP system to provide an accurate forecast of near-surface variables at the subkilometer scale in the Canadian Rocky Mountains in wintertime when the region is fully covered by snow. Observations collected at valley and high-altitude stations are used to evaluate forecast accuracy at three different grid spacing (2.5, 1, and 0.25 km) over a period of 15 days. Decreasing grid spacing was found to improve temperature forecasts at high-altitude stations because of better orography representation. In contrast, no improvement is obtained at valley stations due to an inability of the model to fully capture at all resolutions the intensity of valley cold pools forming during nighttime. Errors in relative humidity reveal that the model tends to overestimate relative humidity at all resolutions, without improvement with decreasing grid spacing. Wind speed forecasts show large improvements with decreasing grid spacing for high-altitude stations exposed to or sheltered from wind. However, no systematic improvement with decreasing grid spacing is found for all stations, which is similar to previous studies. In addition, the model’s sensitivity at subkilometer grid spacing is investigated by evaluating the effects of (i) accounting for additional drag generated by subgrid orographic features, (ii) considering slope angle and aspect on surface radiation, and (iii) using high-resolution initialization for the surface fields.

Global Air–Sea Fluxes of Heat, Fresh Water, and Momentum: Energy Budget Closure and Unanswered Questions

2019 ◽  
Vol 11 (1) ◽  
pp. 227-248 ◽  
Author(s):  
Lisan Yu
Keyword(s):  
Surface Energy ◽  
Fresh Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ocean Surface ◽  
Surface Radiation ◽  
Global Ocean ◽  
Personal Perspective ◽  
Near Surface ◽  
Water Budgets

The ocean interacts with the atmosphere via interfacial exchanges of momentum, heat (via radiation and convection), and fresh water (via evaporation and precipitation). These fluxes, or exchanges, constitute the ocean-surface energy and water budgets and define the ocean's role in Earth's climate and its variability on both short and long timescales. However, direct flux measurements are available only at limited locations. Air–sea fluxes are commonly estimated from bulk flux parameterization using flux-related near-surface meteorological variables (winds, sea and air temperatures, and humidity) that are available from buoys, ships, satellite remote sensing, numerical weather prediction models, and/or a combination of any of these sources. Uncertainties in parameterization-based flux estimates are large, and when they are integrated over the ocean basins, they cause a large imbalance in the global-ocean budgets. Despite the significant progress that has been made in quantifying surface fluxes in the past 30 years, achieving a global closure of ocean-surface energy and water budgets remains a challenge for flux products constructed from all data sources. This review provides a personal perspective on three questions: First, to what extent can time-series measurements from air–sea buoys be used as benchmarks for accuracy and reliability in the context of the budget closures? Second, what is the dominant source of uncertainties for surface flux products, the flux-related variables or the bulk flux algorithms? And third, given the coupling between the energy and water cycles, precipitation and surface radiation can act as twin budget constraints—are the community-standard precipitation and surface radiation products pairwise compatible?

scholarly journals Diagnosing spatial error structures in CO<sub>2</sub> mole fractions and XCO<sub>2</sub> column mole fractions from atmospheric transport

2019 ◽  
Author(s):  
Thomas Lauvaux ◽  
Liza I. Díaz-Isaac ◽  
Marc Bocquet ◽  
Nicolas Bousserez
Keyword(s):  
Atmospheric Transport ◽  
Horizontal Wind ◽  
Ensemble Prediction ◽  
Model Errors ◽  
Spatial Error ◽  
Near Surface ◽  
Sources And Sinks ◽  
The Impact ◽  
Total Column

Abstract. Atmospheric inversions inform about the magnitude and variations of greenhouse gas (GHG) sources and sinks from global to local scales. Deployment of observing systems such as spaceborne sensors and ground-based instruments distributed around the globe has started to offer an unprecedented amount of information to estimate surface exchanges of GHG at finer spatial and temporal scales. However, inversion methods still rely on imperfect atmospheric transport models of which error structures directly affect the inverse estimates of GHG fluxes. The impact of spatial error structures on the inverse fluxes increase concurrently with the density of the available measurements. In this study, we diagnose the spatial structures due to transport model errors affecting modeled in situ carbon dioxide (CO2) mole fractions and total column dry air mole fractions of CO2 (XCO2). We implemented a cost-effective filtering technique recently developed in the meteorological data assimilation community to describe spatial error structures using a small-size ensemble. This technique can enable ensemble-based error analysis for multi-year inversions of sources and sinks. The removal of noisy structures in our small-size ensembles is evaluated by comparison to larger-size ensembles. A second filtering approach for error covariances is proposed (Wiener filter), producing similar results over the 1-month simulation period than a Schur filter. We conclude that key information about error variances and spatial error correlation structures are recoverable from small-size ensembles of about ten (10) members down to five (5), improving the representation of transport errors in mesoscale inversions of CO2 fluxes. Moreover, error variances of in situ near-surface and free-tropospheric CO2 mole fractions differ significantly from total column XCO2 error variances. We conclude that error variances for remote sensing observations need to be quantified independently of in situ CO2 mole fractions due to the complexity of spatial error structures at different altitudes. However, we show the potential use of meteorological error structures such as the mean horizontal wind speed, directly available from Ensemble Prediction Systems, to approximate spatial error correlations of in situ CO2 mole fractions, with similarities in seasonal variations and characteristic error length scales.

scholarly journals Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5268
Author(s):  
Praveena Krishnan ◽  
Tilden P. Meyers ◽  
Simon J. Hook ◽  
Mark Heuer ◽  
David Senn ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Oak Ridge

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

scholarly journals Weather Data Visualization and Analytical Platform

2018 ◽  
Vol 19 (2) ◽  
pp. 79-86
Author(s):  
Hrachya Astsatryan ◽  
Hayk Grogoryan ◽  
Eliza Gyulgyulyan ◽  
Anush Hakobyan ◽  
Aram Kocharyan ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Satellite Image ◽  
Image Data ◽  
Weather Data ◽  
Web Based ◽  
Near Surface ◽  
Satellite Image Processing ◽  
Satellite Image Data ◽  
Monitoring Service

This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period.

A Simple Technique for Creating Regional Composites of Sea Surface Temperature from MODIS for Use in Operational Mesoscale NWP

2010 ◽  
Vol 49 (11) ◽  
pp. 2267-2284 ◽  
Author(s):  
Jason C. Knievel ◽  
Daran L. Rife ◽  
Joseph A. Grim ◽  
Andrea N. Hahmann ◽  
Joshua P. Hacker ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Real Time ◽  
Simple Technique ◽  
Weather Prediction ◽  
Sea Breeze ◽  
Sea Surface ◽  
Horizontal Wind ◽  
Near Surface

Abstract This paper describes a simple technique for creating regional, high-resolution, daytime and nighttime composites of sea surface temperature (SST) for use in operational numerical weather prediction (NWP). The composites are based on observations from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra. The data used typically are available nearly in real time, are applicable anywhere on the globe, and are capable of roughly representing the diurnal cycle in SST. The composites’ resolution is much higher than that of many other standard SST products used for operational NWP, including the low- and high-resolution Real-Time Global (RTG) analyses. The difference in resolution is key because several studies have shown that highly resolved SSTs are important for driving the air–sea interactions that shape patterns of static stability, vertical and horizontal wind shear, and divergence in the planetary boundary layer. The MODIS-based composites are compared to in situ observations from buoys and other platforms operated by the National Data Buoy Center (NDBC) off the coasts of New England, the mid-Atlantic, and Florida. Mean differences, mean absolute differences, and root-mean-square differences between the composites and the NDBC observations are all within tenths of a degree of those calculated between RTG analyses and the NDBC observations. This is true whether or not one accounts for the mean offset between the skin temperatures of the MODIS dataset and the bulk temperatures of the NDBC observations and RTG analyses. Near the coast, the MODIS-based composites tend to agree more with NDBC observations than do the RTG analyses. The opposite is true away from the coast. All of these differences in point-wise comparisons among the SST datasets are small compared to the ±1.0°C accuracy of the NDBC SST sensors. Because skin-temperature variations from land to water so strongly affect the development and life cycle of the sea breeze, this phenomenon was chosen for demonstrating the use of the MODIS-based composite in an NWP model. A simulated sea breeze in the vicinity of New York City and Long Island shows a small, net, but far from universal improvement when MODIS-based composites are used in place of RTG analyses. The timing of the sea breeze’s arrival is more accurate at some stations, and the near-surface temperature, wind, and humidity within the breeze are more realistic.

scholarly journals Daily, Global, High-Resolution SST and Sea Ice Reanalysis for 1985–2007 Using the OSTIA System

2012 ◽  
Vol 25 (18) ◽  
pp. 6215-6232 ◽  
Author(s):  
Jonah Roberts-Jones ◽  
Emma Kathleen Fiedler ◽  
Matthew James Martin
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Optimal Interpolation ◽  
In Situ Observation ◽  
High Latitudes ◽  
Sea Ice Concentration ◽  
Near Surface

Abstract A sea surface temperature (SST) and sea ice reanalysis has been produced at the Met Office based on the Operational SST and Sea Ice Analysis (OSTIA) system. The OSTIA reanalysis produces daily, high-resolution, global foundation SST and sea ice concentration fields from 1 January 1985 to 31 December 2007. The SST reanalysis uses reprocessed satellite and in situ observations that are assimilated using a multiscale optimal-interpolation-type scheme similar to that used in the near-real-time OSTIA system. Validation of the SST analysis using assimilated in situ observation-minus-background statistics shows that the accuracy of the analysis increases throughout the reanalysis period; the global root-mean-square difference is approximately 0.50 K by 2007. This approach to validation is supported in the recent period by results from comparisons with independent near-surface Argo data against which a global standard deviation error of 0.55 K was calculated. Assessment of the OSTIA reanalysis at high latitudes demonstrates that the SST and sea ice fields are more consistent with one another in the Southern Hemisphere than in the Northern Hemisphere. Comparison of the sea ice extents to those in a similar reanalysis shows OSTIA to have larger extents in the Northern Hemisphere, and the Southern Hemisphere extents are similar. The OSTIA reanalysis SSTs are shown to be regionally comparable with similar reanalyses, with the largest differences occurring at high latitudes in the summer hemisphere. Differences are observed around the ice edge and in regions with high SST gradients. The OSTIA reanalysis provides a valuable high-resolution addition to the satellite period SST data record that makes use of the (Advanced) Along-Track Scanning Radiometer [(A)ATSR] multimission data.

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