Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and Precipitation in Tropical Cyclone Nanmadol from GMI Data

Understanding the structure of tropical cyclone(TC)hydrometeors is crucial for detecting the changes in the distribution and intensity of precipitation.In this study,the GMI brightness temperature and cloud-dependent 1DVAR algorithm were used to retrieve the hydrometeor profiles and surface rain rate of TC Nanmadol(2022).The Advanced Radiative Transfer Modeling System(ARMS)was used to calculate the Jacobian and degrees of freedom(△DOF)of cloud water,rainwater,and graupel for different channels of GMI in convective conditions.The retrieval results were compared with the Dual-frequency Precipitation Radar(DPR),GMI 2A,and IMERG products.It is shown that from all channels of GMI,rain water has the highest△DOF,at 1.72.According to the radiance Jacobian to atmospheric state variables,cloud water emission dominates its scattering.For rain water,the emission of channels 1–4 dominates scattering.Compared with the GMI 2A precipitation product,the 1DVAR precipitation rate has a higher correlation coefficient(0.713)with the IMERG product and can better reflect the location of TC precipitation.Near the TC eyewall,the highest radar echo top indicates strong convection.Near the melting layer where Ka-band attenuation is strong,the double frequency difference of DPR data reflects the location of the melting.The DPR drop size distribution(DSD)product shows that there is a significant increase in particle size below the melting layer in the spiral rain band.Thus,the particle size may be one of the main reasons for the smaller rain water below the melting layer retrieved from 1DVAR.

Optimal Assimilation of Microwave Upper-Level Sounding Data in CMA-GFS

Various approaches have been proposed to minimize the upper-level systematic biases in global numerical weather prediction(NWP)models by using satellite upper-air sounding channels as anchors.However,since the China Meteorological Administration Global Forecast System(CMA-GFS)has a model top near 0.1 hPa(60 km),the upper-level temperature bias may exceed 4 K near 1 hPa and further extend to 5 hPa.In this study,channels 12–14 of the Advanced Microwave Sounding Unit A(AMSU-A)onboard five satellites of NOAA and METOP,whose weighting function peaks range from 10 to 2 hPa are all used as anchor observations in CMA-GFS.It is shown that the new“Anchor”approach can effectively reduce the biases near the model top and their downward propagation in three-month assimilation cycles.The bias growth rate of simulated upper-level channel observations is reduced to±0.001 K d^(–1),compared to–0.03 K d^(–1)derived from the current dynamic correction scheme.The relatively stable bias significantly improves the upper-level analysis field and leads to better global medium-range forecasts up to 10 days with significant reductions in the temperature and geopotential forecast error above 10 hPa.

Comparison of tropical cyclone thermal structures derived from ATMS and synthetic AMSU-A/MHS

热带气旋(TC)的暖心反映了其强度和变化.微波探测数据被广泛用于探测TC暖心,但观测到的TC暖心强度可能因仪器而异.本研究利用先进技术微波探测仪(ATMS)的过采样数据重采样至与先进微波探测仪(AMSU-A)一致的亮温,称为类AMSU数据.通过对比发现,ATMS的观测更加细致,较好地刻画了TC眼区和云带.使用ATMS和类-AMSU数据反演多个TC的暖心发现,在250 hPa,使用ATMS数据反演的暖心强度比类-AMSU高约1-2K,并且其暖心结构更详细.

Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors.The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally,without considering the three-dimensional distribution of clouds.Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach.In this study,the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2(MWTS-Ⅱ)onboard the Fengyun-3D,which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters.Cycling data assimilation and forecasting experiments for Typhoons Lekima(2019)and Mitag(2019)are carried out.Compared with the control experiment,the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%.The quality of the additional MWTS-Ⅱradiance data is close to the clear-sky data.The case studies show that the average root-mean-square errors(RMSE)of prognostic variables are reduced by 1.7%in the upper troposphere,leading to an average reduction of4.53%in typhoon track forecasts.The detailed diagnoses of Typhoon Lekima(2019)further show that the additional MWTS-Ⅱradiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation,thus providing more precise structures.This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

Improved Microwave Ocean Emissivity and Reflectivity Models Derived from Two-Scale Roughness Theory

The Geometrical Optics(GO)approach and the FAST Emissivity Model(FASTEM)are widely used to estimate the surface radiative components in atmospheric radiative transfer simulations,but their applications are limited in specific conditions.In this study,a two-scale reflectivity model(TSRM)and a two-scale emissivity model(TSEM)are developed from the two-scale roughness theory.Unlike GO which only computes six non-zero elements in the reflectivity matrix,The TSRM includes 16 elements of Stokes reflectivity matrix which are important for improving radiative transfer simulation accuracy in a scattering atmosphere.It covers the frequency range from L-to W-bands.The dependences of all TSRM elements on zenith angle,wind speed,and frequency are derived and analyzed in details.For a set of downwelling radiances in microwave frequencies,the reflected upwelling brightness temperature(BTs)are calculated from both TSRM and GO and compared for analyzing their discrepancies.The TSRM not only includes the effects of GO but also accounts for the small-scale Bragg scattering effect in an order of several degrees in Kelvins in brightness temperature.Also,the third and fourth components of the Stokes vector can only be produced from the TSRM.For the emitted radiation,BT differences in vertical polarization between a TSEM and FASTEM are generally less than 5 K when the satellite zenith angle is less than 40°,whereas those for the horizontal component can be quite significant,greater than 20 K.

Advanced Radiative Transfer Modeling System(ARMS): A New-Generation Satellite Observation Operator Developed for Numerical Weather Prediction and Remote Sensing Applications

1.Urgent requirements for developing Feng Yun satellite observation operators In the past two decades,satellite measurements have been widely utilized for understanding and predicting weather and climate,and are now an essential component in global observing systems.

Applications of AMSR-E Measurements for Tropical Cyclone Predictions PartⅠ: Retrieval of Sea Surface Temperature and Wind Speed

Existing satellite microwave algorithms for retrieving Sea Surface Temperature （SST） and Wind （SSW） are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System （EOS） Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer （AMSRoE） onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSRoE measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square （RMS） errors for SST and SSW are about 1.8 K and 1.9 m s^- 1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds （e.g., its liquid water path is larger than 0.5 mm）, while they are 1.1 K for SST and 2.0 m s^-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.

An Observational Study on the Local Climate Effect of the Shangyi Wind Farm in Hebei Province

Zhangjiakou is an important wind power base in Hebei Province,China.The impact of its wind farms on the local climate is controversial.Based on long-term meteorological data from 1981 to 2018,we investigated the effects of the Shangyi Wind Farm(SWF)in Zhangjiakou on air temperature,wind speed,relative humidity,and precipitation using the anomaly or ratio method between the impacted weather station and the non-impacted background weather station.The influence of the SWF on land surface temperature(LST)and evapotranspiration(ET)using MODIS satellite data from 2003 to 2018 was also explored.The results showed that the SWF had an atmospheric warming effect at night especially in summer and autumn(up to 0.95℃).The daytime air temperature changes were marginal,and their signs were varying depending on the season.The annual mean wind speed decreased by 6%,mainly noted in spring and winter(up to 14%).The precipitation and relative humidity were not affected by the SWF.There was no increase in LST in the SWF perhaps due to the increased vegetation coverage unrelated to the wind farms,which canceled out the wind farm-induced land surface warming and also resulted in an increase in ET.The results showed that the impact of wind farms on the local climate was significant,while their impact on the regional climate was slight.

Use of Incremental Analysis Updates in 4D-Var Data Assimilation

The four-dimensional variational （4D-Var） data assimilation systems used in most operational and research centers use initial condition increments as control variables and adjust initial increments to find optimal analysis solutions. This approach may sometimes create discontinuities in analysis fields and produce undesirable spin ups and spin downs. This study explores using incremental analysis updates （IAU） in 4D-Var to reduce the analysis discontinuities. IAU-based 4D-Var has almost the same mathematical formula as conventional 4D-Var if the initial condition increments are replaced with time-integrated increments as control variables. The IAU technique was implemented in the NASA/GSFC 4D-Var prototype and compared against a control run without IAU. The results showed that the initial precipitation spikes were removed and that other discontinuities were also reduced, especially for the analysis of surface temperature.

Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System:Sequential Implementation

This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background （OmB） residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis （OmA） are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.

Vector Radiative Transfer in a Vertically Inhomogeneous Scattering and Emitting Atmosphere.PartⅠ:A New Discrete Ordinate Method

The original vector discrete ordinate radiative transfer(VDISORT)model takes into account Stokes radiance vector but derives its solution assuming azimuthal symmetric surface reflective matrix and atmospheric scattering phase matrix,such as the phase matrix derived from spherical particles or randomly oriented non-spherical particles.In this study,a new VDISORT is developed for general atmospheric scattering and boundary conditions.Stokes vector is decomposed into both sinusoidal and cosinusoidal harmonic modes,and the radiance at arbitrary viewing geometry is solved directly by adding two zero-weighted points in the Gaussian quadrature scheme.The complex eigenvalues in homogeneous solutions are also taken into full consideration.The accuracy of VDISORT model is comprehensively validated by four cases:Rayleigh scattering case,the spherical particle scattering case with the Legendre expansion coefficients of 0th-13th orders of the phase matrix(hereinafter L13),L13 with a polarized source,and the randomoriented oblate particle scattering case with the Legendre expansion coefficients of 0th-11th orders of the phase matrix(hereinafter L11).In all cases,the simulated radiances agree well with the benchmarks,with absolute biases less than 0.0065,0.0006,and 0.0008 for Rayleigh,unpolarized L13,and L11,respectively.Since a polarized bidirectional reflection distribution function(pBRDF)matrix is used as the lower boundary condition,VDISORT is now able to handle fully coupled atmospheric and surface polarimetric radiative transfer processes.

Vector Radiative Transfer in a Vertically Inhomogeneous Scattering and Emitting Atmosphere. Part Ⅱ: Coupling with Azimuthally Asymmetric Polarized Bidirectional Reflection over Oceans

The reflection of ocean surface is often assumed azimuthally symmetric in the previous vector discrete ordinate radiative transfer(VDISORT)and many other radiative transfer solvers.This assumption can lead to obvious errors in the simulated radiances.In this study,the vector radiative transfer equation is solved with a polarized bidirectional reflection distribution function(pBRDF)for computing the surface-leaving radiation from the lower boundary.An azimuthally asymmetric pBRDF model at visible and infrared bands over oceans is fully coupled with the updated VDISORT model.The radiance at the ocean surface is combined with the contributions of atmospheric scattering and surface properties.It is shown that the radiance at the ocean surface also exhibits a strong angular dependence in the Stokes vector and the magnitudes of I.Q.and V increase for a larger azimuthal dependence of pBRDF.In addition,the solar position affects the peaks of sun glitter pattern,thus modulating the signal magnitudes and the angular distributions.As ocean wind increases,the reflection weakens with reduced magnitudes of Stokes parameters and lessvarying angular distributions.

Random Forest-Based Snow Cover Mapping in China Using Fengyun-3B VIRR Data

Currently,there is variability in the spectral band thresholds for snow cover recognition using remote sensing in different regions and for complex terrains.Using Fengyun-3B Visible and Infra-Red Radiometer(FY-3B VIRR)satellite data,we applied random forest(RF)methodology and selected 13 feature variables to obtain snow cover.A training set was generated,containing approximately 1 million snow and nonsnow samples obtained in China from the snow monitoring reports issued by the National Satellite Meteorological Centre and four snow cover products from the Interactive Multi-sensor Snow and Ice Mapping System(IMS),the FY-3B Multi-Sensor Synergy(MULSS),the Moderate Resolution Imaging Spectroradiometer(MODIS)snow cover product(MYD10A1),and the National Cryosphere Desert Data Center(NCDC).This training set contained many different samples of cloud types and snow under forest cover to help effectively distinguish snow and clouds and improve the recognition rate of snow under forest cover.Then,two RF snow cover recognition models were constructed for the snow and nonsnow seasons and they were used to conduct daily snow cover recognition in China from 2011 to 2020.The results show that the RF models constructed based on FY-3B VIRR data have good recognition performance for shallow snow,understory snow,and snow on the Qinghai–Tibetan Plateau.The recognition accuracy against weather stations and the spatial consistency with the IMS product are better than the MULSS,MYD10A1,and NCDC products.The overall accuracy of the RF product is 90.6%,and the recall rate is 93.8%.The omission and commission errors are 6.2%and11.1%,respectively.Unlike other existing snow cover algorithms,the established RF model skips the complicated atmospheric correction and cloud identification processes and does not involve external auxiliary data;thus,it is more easily popularized and operationally applicable to generating long-time series snow cover products.

Remote Sensing of Tropical Cyclone Thermal Structure from Satellite Microwave Sounding Instruments: Impacts of Background Profiles on Retrievals

A variational retrieval system often requires background atmospheric profiles and surface parameters in its minimization process. This study investigates the impacts of specific background profiles on retrievals of tropical cyclone(TC) thermal structure. In our Microwave Retrieval Testbed(MRT), the K-means clustering algorithm is utilized to generate a set of mean temperature and water vapor profiles according to stratiform and convective precipitation in hurricane conditions. The Advanced Technology Microwave Sounder(ATMS) observations are then used to select the profiles according to cloud type. It is shown that the cloud-based background profiles result in better hurricane thermal structures retrieved from ATMS observations. Compared to the Global Positioning System(GPS) dropsonde observations, the temperature and specific humidity errors in the TC inner region are less than 3 K and 2.5 g kg^(–1), respectively, which are significantly smaller than the retrievals without using the cloud-based profiles. Further experiments show that all the ATMS observations could retrieve well both temperature and humidity structures, especially within the inner core region. Thus, both temperature and humidity profiles derived from microwave sounding instruments in hurricane conditions can be reliably used for evaluation of the storm intensity with a high fidelity.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.

A Multivariable Approach for Estimating Soil Moisture from Microwave Radiation Imager(MWRI)

Accurate measurements of soil moisture are beneficial to our understanding of hydrological processes in the earth system. A multivariable approach using the random forest(RF) machine learning technique is proposed to estimate the soil moisture from Microwave Radiation Imager(MWRI) onboard Fengyun-3 C satellite. In this study, Soil Moisture Operational Products System(SMOPS) products disseminated from NOAA are used as a truth to train the algorithm with the input of MWRI brightness temperatures(TBs) at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz, TB polarization ratios(PRs) at 10.65, 18.7, and 23.8 GHz, height in digital elevation model(DEM), and soil porosity. The retrieved soil moisture is also validated against the independent SMOPS data, and the correlation coefficient is about0.8 and mean bias is 0.002 m^3 m^-3 over the period from 1 August 2017 to 31 May 2019. Our retrieval of soil moisture also has a higher correlation with ECMWF ERA5 soil moisture data than the MWRI operational products. In the western part of China, the spatial distribution of MWRI soil moisture is much improved, compared to the MWRI operational products.

Remote Sensing of Tropical Cyclone Thermal Structure from Satellite Microwave Sounding Instruments:Impacts of Optimal Channel Selection on Retrievals

Accurate information on atmospheric temperature of tropical cyclones （TCs） is important for monitoring and pre- diction of their developments and evolution. For hurricanes, temperature anomaly in the upper troposphere can be de-rived from Advanced Microwave Sounding Unit （AMSU） and Advanced Technology Microwave Sounder （ATMS） through either regression-based or variational retrieval algorithms. This study investigates the dependency of TC warm core structure on emission and scattering processes in the forward operator used for radiance computations in temperature retrievals. In particular, the precipitation scattering at ATMS high-frequency channels can significantly change the retrieval outcomes. The simulation results in this study reveal that the brightness temperatures at 183 GHz could be depressed by 30-50 K under cloud ice water path of 1.5 mm, and thus, the temperature structure in hur-ricane atmosphere could be distorted if the ice cloud scattering was inaccurately characterized in the retrieval system. It is found that for Hurricanes Irma, Maria, and Harvey that occurred in 2017, their warm core anomalies retrieved from ATMS temperature sounding channels 4 15 were more reasonable and realistic, compared with the retrievals from all other channel combinations and earlier hurricane simulation results.

Verification of Fengyun-3D MWTS and MWHS Calibration Accuracy Using GPS Radio Occultation Data

The newly launched Fengyun-3D(FY-3D)satellite carries microwave temperature sounder(MWTS)and microwave humidity sounder(MWHS),providing the global atmospheric temperature and humidity measurements.It is important to assess the in orbit performance of MWTS and MWHS and understand their calibration accuracy before using them in numerical weather prediction and many other applications such as hurricane monitoring.This study aims at quantifying the biases of MWTS and MWHS observations relative to the simulations from the collocated Global Positioning System(GPS)radio occultation(RO)data.Using the collocated FY-3C Global Navigation Satellite System Occultation Sounder(GNOS)RO data under clear-sky conditions as inputs to Community Radiative Transfer Model(CRTM),brightness temperatures and viewing angles are simulated for the upper level sounding channels of MWTS and MWHS.In order to obtain O–B statistics under clear sky conditions,a cloud detection algorithm is developed by using the two MWTS channels with frequencies at 50.3 and 51.76 GHz and the two MWHS channels with frequencies centered at 89 and 150 GHz.The analysis shows that for the upper air sounding channels,the mean biases of the MWTS observations relative to the GPS RO simulations are negative for channels 5–9,with absolute values<1 K,and positive for channels 4 and 10,with values<0.5 K.For the MWHS observations,the mean biases in brightness temperature are negative for channels 2–6,with absolute values<2.6 K and relatively small standard deviations.The mean biases are also negative for channels 11–13,with absolute values<1.3 K,but with relatively large standard deviations.The biases of both MWTS and MWHS show scan-angle dependence and are asymmetrical across the scan line.The biases for the upper air MWTS and MWHS sounding channels are larger than those previously derived for the Advanced Technology Microwave Sounder.

Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands

How does the urban spatial landscape(USL)pattern affect the land surface urban heat islands(SUHIs)and canopy urban heat islands(CUHIs)?Based on satellite and meteorological observations,this case study compares the impacts of the USL pattern on SUHI and CUHI in the central urban area(CUA)of Beijing using the satellite land-surface-temperature product and hourly temperature data from automatic meteorological stations from 2009 to 2018.Eleven USL metrics—building height(BH),building density(BD),standard deviation of building height(BSD),floor area ratio(FAR),frontal area index(FAI),roughness length(RL),sky view factor(SVF),urban fractal dimension(FD),vegetation coverage(VC),impervious coverage(IC),and albedo(AB)—with a 500-m spatial resolution in the CUA are extracted for comparative analysis.The results show that SUHI is higher than CUHI at night,and SUHI is only consistent with CUHI at spatial-temporal scales at night,particularly in winter.Spatially,all 11 metrics are strongly correlated with both the SUHI and CUHI at night,with stronger correlation between most metrics and SUHI.VC,AB,and SVF have the greatest impact on both the SUHI and CUHI.High SUHI and CUHI values tend to appear in areas with BD≥0.26,VC≤0.09,AB≤0.09,and SVF≤0.67.In summer,most metrics have a greater impact on the SUHI than CUHI;the opposite is observed in winter.SUHI variation is affected primarily by VC in summer and by VC and AB in winter,which is different for the CUHI variation.The collective contribution of all 11metrics to SUHI spatial variation in summer(61.8%)is higher than that to CUHI;however,the opposite holds in winter and for the entire year,where the cumulative contribution of the factors accounts for 66.6%and 49.6%,respectively,of the SUHI variation.

Influences of Physical Processes and Parameters on Simulations of TOA Radiance at UV Wavelengths: Implications for Satellite UV Instrument Validation

Numerous factors can influence the radiative transfer simulation of hyper-spectral ultraviolet satellite observation,including the radiative transfer scheme, gaseous absorption coefficients, Rayleigh scattering scheme, surface reflectance, aerosol scattering, band center wavelength shifts of sensor, and accuracy of input profiles. In this study, a Unified Linearized Vector Radiative Transfer Model(UNL-VRTM) is used to understand the influences of various factors on the top of atmosphere(TOA) normalized radiance in the ultraviolet(UV) region. A benchmark test for Rayleigh scattering is first performed to verify the UNL-VRTM accuracy, showing that the model performances agree well with earlier peer-reviewed results. Sensitivity experiments show that a scalar radiative transfer approximation considering only ozone and a constant surface reflectance within the UV region may cause significant errors to the TOA normalized radiance. A comparison of the Ozone Mapping and Profiler Suite(OMPS) radiances between simulations and observations shows that the surface reflectance strongly influences the accuracy for the wavelengths larger than 340 nm. Thus, using the surface reflectivity at 331 nm as a proxy for simulating the whole OMPS hyperspectral ultraviolet radiances is problematic. The impact of rotational Raman scattering on TOA radiance can be simulated through using SCIATRAN, which can also reduce the difference between measurements and simulations to some extent. Overall, the differences between OMPS simulations and observations can be less than 3% for the entire wavelengths. The bias is nearly constant across the cross-track direction.