Observation Minus Background Research Articles

Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system

Abstract. The microwave radiances are key observations, especially over data-sparse regions, for operational data assimilation in numerical weather prediction (NWP). An often applied simplification is that these observations are used as point measurements; however, the satellite field of view may cover many grid points of high-resolution models. Therefore, we examine a solution in high-resolution data assimilation to better account for the spatial representation of the radiance observations. This solution is based on a footprint operator implemented and tested in the variational assimilation scheme of the AROME-Arctic (Application of Research to Operations at MEsoscale – Arctic) limited-area model. In this paper, the design and technical challenges of the microwave radiance footprint operator are presented. In particular, implementation strategies, the representation of satellite field-of-view ellipses, and the emissivity retrieval inside the footprint area are discussed. Furthermore, the simulated brightness temperatures and the sub-footprint variability are analysed in a case study, indicating particular areas where the use of the footprint operator is expected to provide significant added value. For radiances measured by the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) sensors, the standard deviation of the observation minus background (OmB) departures is computed over a short period in order to compare the statistics of the default and the implemented footprint observation operator. For all operationally used AMSU-A and MHS tropospheric channels, it is shown that the standard deviation of OmB departures is reduced when the footprint operator is applied. For AMSU-A radiances, the reduction is around 1 % for high-peaking channels and about 4 % for low-peaking channels. For MHS data, this reduction is somewhere between 1 %–2 % by the footprint observation operator.

Quantifying the Impact of Aerosols on Geostationary Satellite Infrared Radiance Simulations: A Study with Himawari-8 AHI

Aerosols exert a significant influence on the brightness temperature observed in the thermal infrared (IR) channels, yet the specific contributions of various aerosol types remain underexplored. This study integrated the Copernicus Atmosphere Monitoring Service (CAMS) atmospheric composition reanalysis data into the Radiative Transfer for TOVS (RTTOV) model to quantify the aerosol effects on brightness temperature (BT) simulations for the Advanced Himawari Imager (AHI) aboard the Himawari-8 geostationary satellite. Two distinct experiments were conducted: the aerosol-aware experiment (AER), which accounted for aerosol radiative effects, and the control experiment (CTL), in which aerosol radiative effects were omitted. The CTL experiment results reveal uniform negative bias (observation minus background (O-B)) across all six IR channels of the AHI, with a maximum deviation of approximately −1 K. Conversely, the AER experiment showed a pronounced reduction in innovation, which was especially notable in the 10.4 μm channel, where the bias decreased by 0.7 K. The study evaluated the radiative effects of eleven aerosol species, all of which demonstrated cooling effects in the AHI’s six IR channels, with dust aerosols contributing the most significantly (approximately 86%). In scenarios dominated by dust, incorporating the radiative effect of dust aerosols could correct the brightness temperature bias by up to 2 K, underscoring the substantial enhancement in the BT simulation for the 10.4 μm channel during dust events. Jacobians were calculated to further examine the RTTOV simulations’ sensitivity to aerosol presence. A clear temporal and spatial correlation between the dust concentration and BT simulation bias corroborated the critical role of the infrared channel data assimilation on geostationary satellites in capturing small-scale, rapidly developing pollution processes.

Assessment for operational assimilation of horizontal line of sight winds from the European Space Agency's Aeolus at the Met Office

AbstractThe European Space Agency's Aeolus satellite was launched in August 2018 and began delivering horizontal line‐of‐sight (HLOS) wind observations in early September 2018. In early 2019, the Met Office began assessing the suitability of the HLOS winds for operational assimilation into its global numerical weather prediction system. We performed a number of assimilation experiments to assess the impact of HLOS wind observations on our global forecasts. We have found that assimilating HLOS winds changes the zonal winds in the analysis fields predominantly in the Tropics and Southern Hemisphere, with the largest changes being in the upper troposphere and lower stratosphere. This has a positive impact on the accuracy of the global weather forecasts, with improvements in the root‐mean‐square error seen throughout the troposphere. Assimilation of Aeolus HLOS winds improves the standard deviation of the observation minus background (a 6 hr forecast) of almost all other observation types, suggesting that the numerical weather prediction model analysis is improved, which consequently improves the 6 hr forecast. In a set of short‐period observation denial experiments, we found that assimilating Aeolus has an impact similar in magnitude to assimilating surface winds from scatterometers. Assimilating winds from the Rayleigh channel has approximately three times the impact that assimilating HLOS winds from the Mie channel does. Both channels contribute a measureable improvement to the global forecast, and we therefore started operational assimilation of winds from the Mie channel in December 2020 and the Rayleigh channel operationally in May 2022.

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations.

The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O-B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O-B and Gaussian distributions when assimilating all-sky data from FY-4A/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A/AGRI observations and improves the Gaussianity of the O-B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A/AGRI observations in a uniform manner without cloud detection.

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-Sensor Consistency Using Radiative Transfer Modeling

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

Primary Impact Evaluation of Surface Temperature Observations for Microwave Temperature Sounding Data Assimilation over Land

Observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites are considered to be the most effective satellite data in terms of obviously reducing operational prediction errors. However, there are still significant difficulties in the application of AMSU-A low-level channel data assimilation over land. One of them is the inaccurate surface skin temperature (SKT) of the background on land areas, which leads to significant uncertainty in the accuracy of simulating brightness temperature (BT) in these channels. Therefore, improving the accuracy of SKT in the background field is a direct way to improve the assimilation effect of these low-level channel data over land. In this study, both high-spatio-temporal-resolution automatic weather station (AWS) observation data from China in September 2021 and the AMSU-A observation data from NOAA-15/18/19 and MetOp-A were used. Based on the Advanced Research version of the Weather Research and Forecast model (WRF-ARW) and Gridpoint Statistical Interpolation (GSI) assimilation system, we first analyzed the differences in SKT between AWS observations and model simulations and then attempted to directly replace the simulated SKT with the observation data. On this basis, the differences in BT simulation effects over the land area of Southwest China before and after replacement were meticulously analyzed and compared. In addition, the impacts of SKT replacement in areas with different terrain elevations and in cloudy areas were also evaluated. The results indicate that the SKTs of background fields were generally lower than the surface observations, whereas the diurnal variation in SKT was not well simulated. After replacing the SKT of the background field with station observations, the BT differences between the observation and background (O–B, observation minus background) were remarkably reduced, especially for channels 3–5 and 15 of the AMSU-A. The volume of data passing the GSI quality control significantly increased, and the standard deviation of O–B decreased. Further analysis showed that the improvement effect was better in areas at an elevation above 1600 m. Moreover, introducing SKT observations leads to a significant and stable improvement over BT simulations in cloudy areas over land.

Characterization of Himawari-8/AHI to Himawari-9/AHI infrared observations continuity

ABSTRACT As the ‘twin imagers’, Himawari-8/AHI and Himawari-9/AHI are both the new generation imagers operated by the Japan Meteorological Agency (JMA). After more than 7 years of operation, Himawari-8/AHI turned into stand-by status and Himawari-9/AHI began operation as the successor. To continuous users of both imagers, it is essential to characterize continuity of Himawari-8/AHI to Himawari-9/AHI observations, which can provide beneficial information to quantitative retrieval and satellite assimilation algorithm developer. In this research, cross-comparison are performed to Himawari-8/AHI and Himawari-9/AHI infrared (IR) observations, and ‘observation minus background (O - B)’ characters of two imagers under clear-sky condition over ocean are also compared. The results are as follows: 1) Himawari-8/AHI and Himawari-9/AHI IR observations are in good consistency with largest absolute bias less than 1.437 K and RMSE below 1.663 K. Biases and RMSEs for Himawari-8/AHI and Himawari-9/AHI channel 7 show obvious diurnal variation character and RMSEs for all channels have evident temporal cycle variation characteristics, with cycle time of about 10 days; 2) IR observations of Himawari-8/AHI are more closed to RTTOV simulations under clear-sky condition than those of Himawari-9/AHI, especially for channels 7, 9 and 16. Dependence of O – B biases and RMSEs for two imagers IR channels on satellite scan angles are almost identical.

Impact of assimilating Aeolus observations in the global model ICON: A global statistical overview

AbstractGlobal wind profiles provided by the satellite mission Aeolus are an important recent supplement to the Global Observing System. This study investigates the impact of Aeolus horizontal line‐of‐sight wind observations in the operational global assimilation and forecasting system of Deutscher Wetterdienst that is based on the Icosahedral Nonhydrostatic (ICON) model. For this purpose, an observing system experiment was conducted and evaluated for a 3‐month period from July 2020 to October 2020. The Aeolus Rayleigh clear and Mie cloudy data quality and consistency were derived from observation minus background statistics. To correct for an altitude‐dependent bias, a model‐based bias correction scheme has been implemented. Comparisons of the systematic changes in the analysis and the respective forecasts provide an overview of the overall impact of the Aeolus horizontal line‐of‐sight wind assimilation in ICON. Increased influence of Aeolus wind profiles is found in jet regimes (e.g., amplification of the zonal wind component), around large‐scale circulation systems, and convectively active areas in the Tropics. The reduction in forecast error is largest in the tropical upper troposphere and stratosphere, as well as in the mid and upper troposphere of the Southern Hemisphere. The Northern Hemisphere shows a somewhat smaller but still beneficial impact of Aeolus observations. The verification with other conventional observations shows a mean relative reduction in short‐range forecast error between 0.1% and 0.6% in the Northern Hemisphere and up to 1.6% in the Tropics and the Southern Hemisphere. When verifying against the European Centre for Medium‐Range Weather Forecast Reanalysis v5, forecast errors of zonal wind, temperature, and geopotential up to 5 days lead time are reduced by 2–4% on global average and up to 5–8% around the tropical tropopause.

Impact of Assimilating GK-2A All-Sky Radiance with a New Observation Error for Summer Precipitation Forecasting

In the assimilation of all-sky radiance (ASR), the non-Gaussian behaviour of observation-minus-background (OMB) departures has been the major issue. Treating observation error properly should give the distribution OMB departures closer to Gaussian on which data assimilation systems are based. This study introduces a look-up-table (LUT) observation error inflation (LOEI) for assimilating ASR from three water vapor channels of GEO-KOMPSAT-2A (GK-2A) geostationary satellite based on a three-dimensional variational data assimilation (3DVAR) framework. The impacts are assessed based on summer precipitation cases over South Korea. To confirm all kinds of radiance observations, the ASRs are assimilated without any quality control procedures. The LOEI adopt a pre-estimated radiance error statistics by using the higher order fitting function of cloud amount (CA) and standard deviation (STD) of OMB departures. This LOEI was produced during the summer period from August 1 to 30, 2020, representing the characteristics of the atmosphere condition during the experimental period. The promising impact of LOEI is demonstrated in comparison with the inflated observation error using a simple linier function proposed by Geer and Bauer (GBOEI). Study results revealed the LOEI normalized OMB departures into much more Gaussian form than the GBOEI. Hence, the assimilation of ASR using LOEI (ExpLOEI) produced BT analysis closer to the observation in four cloud phases in contrast with ASR assimilation using GBOEI (ExpGBOEI), which obviously found in the ice phase. The better BT analysis eventually simulated more realistic moisture and temperature variables in the background field. Consequently, the ExpLOEI exhibited more accuracy in precipitation location and intensity compared to the experiment with ExpGBOEI.

Nonlinear Bias Correction of the FY-4A AGRI Infrared Radiance Data Based on the Random Forest

Bias correction is a key prerequisite for radiance data assimilation. Directly assimilating the radiance observations generally involves large systematic biases affecting the numerical prediction accuracy. In this study, a nonlinear bias correction scheme with Random Forest (RF) technology is firstly proposed based on the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) channels 9–10 observations in the Weather Research and Forecasting Data Assimilation (WRFDA) system. Two different settings of the predictors are additionally designed and evaluated based on the performance of the RF model. It seems that an apparent scene temperature-dependent bias could be effectively resolved by the RF scheme when applying the RF method with newly added predictors. Results suggest that the proposed nonlinear scheme of RF performs better than the linear scheme does in terms of reducing the systematic biases. A more idealized error distribution of observation minus background (OMB) is found in the RF-based experiments that measure the nonlinear relationship between the OMB biases and the predictors when using the Gaussian distribution as the reference. Furthermore, the RF scheme shows a consistent improvement in bias correction with the potential to ameliorate the atmospheric variables of analyses.

Biases’ Characteristics Assessment of the HY-2B Scanning Microwave Radiometer (SMR)’s Observations

The second Chinese ocean dynamic environment satellite Haiyang-2B (HY-2B), carrying a scanning microwave radiometer (SMR) to provide information on the ocean and atmosphere, was successfully launched on 25 October 2018. Before the data assimilation, it is necessary to characterize and evaluate the biases of the HY-2B SMR observations. This study is the first to conduct a systematic assessment of the SMR radiance data based on observation minus background simulation (O-B). Three types of numerical weather prediction (NWP) datasets, including ECMWF Reanalysis v5 (ERA5), the analysis fields from the NCEP Global Forecast System (NCEP-GFS), and the analysis fields from the Global Regional Assimilation and Prediction System-Global Forecast System (GRAPES-GFS), were used as input information for RTTOV v12.3 to simulate the SMR’s observed brightness temperature (TB) under clear-sky conditions. Study results showed that the O-B biases and IQR of the SMR for most channels were within −2.5–0.4 K and smaller than 4 K, respectively. The SMR observations were generally consistent with the RTTOV simulations, even based on the different NWP fields. These results indicate a good prospect for the assimilated application of HY-2B SMR radiance data. However, due to the impact of RFI, the SMR’s descending data for two 10.7 GHz channels showed some significant positive biases larger than 50 K over the seas of the European region. In addition, it seems that the bias characteristics of the SMR’s ascending data were obviously different from those of the descending data. It was also found that the variation trend of scan-position-dependent bias was generally stable for the SMR’s ascending data but fluctuates significantly for the descending data, with a maximum amplitude greater than 0.7 K for some channels.

The impacts of assimilating CFOSAT scatterometer winds for Typhoon cases based on real-time rain quality control

Rain is the main factor influencing the Ku-band scatterometer wind quality. The operational quality control (QC) scheme of the scatterometer wind retrieval procedure cannot effectively distinguish rain from high winds, limiting the optimal assimilation of Ku-band scatterometer winds in numerical weather prediction. To compensate for the inadequate operational QC of CFOSAT scatterometer (CSCAT) data, a real-time rain quality control (RRQC) method in data assimilation was proposed. Based on real-time rain forecasts, the RRQC method aims to screen and eliminate rain-affected CSCAT data. To begin, the impact of rain on CSCAT data for the entire year 2019 was analyzed. The CSCAT wind speed standard deviation (STD) and bias are found to increase with rain rate, and when the rain rate exceeds 2.5 mm/h, the STD is greater than the CSCAT-designed accuracy (2 m/s). Following that, two Typhoon cases, namely Lekima (2019) and In-fa (2021), were studied to assess the RRQC method's impact on Typhoon forecasting. It shows that assimilating CSCAT data with the RRQC method improves wind analysis and forecast, and reduces the mean track error. Finally, Typhoon Lekima was further diagnosed. It is found that the CSCAT data eliminated by RRQC are mainly distributed around the Lekima rainband with large wind speed observation minus background (OMB) absolute values. The results indicate that RRQC can identify rain contaminations in the CSCAT data and thus improve the analysis and forecast.

Improved Estimation of O-B Bias and Standard Deviation by an RFI Restoration Method for AMSR-2 C-Band Observations over North America

Spaceborne microwave radiometer observations play vital roles in surface parameter retrievals and data assimilation, but widespread radio-frequency interference (RFI) signals in the C-band channel result in a lack of valuable data over large areas. Establishing repaired data based on existing observation information is crucial. In this study, Advanced Microwave Scanning Radiometer (AMSR)-2 C-band data affected by RFI were accurately repaired through the iterative principal component analysis (PCA) method in 2016 over the U.S. land area. The standard deviation (STD) and bias characteristics of the brightness temperature in the C-band vertical polarization channel were compared and analyzed before and after the restoration to verify the assimilation application prospect of the repaired data. Not only was the spatial continuity of the microwave imager observations significantly improved following restoration; the STD and bias of the observation minus background (OMB) of the restored data were basically consistent with those of the RFI-free data. The STD of OMB exhibited obvious seasonal variations, which were approximately 4.0 K from January to May and 3.0 K from June to December, whereas the biases were near zero in winter but negative (approximately −2.0 K) in summer. The surface type and terrain height also critically affected the STD and bias. The STD decreased with increasing terrain height, whereas the bias exhibited the opposite trend. The STD was largest in low-vegetation areas (4.0 K) but only approximately 2.0–3.0 K in pine forest and brush areas. These results show that the restored data have a high prospect for retrieval application and assimilation, and the STD and bias estimation results also provide a reference for land-based AMSR-2 data assimilation.

Impact of the Aeolus Level‐2B horizontal line‐of‐sight winds in the Environment and Climate Change Canada global forecast system

AbstractThe European Space Agency Aeolus mission was launched in August 2018. This satellite carries the first Doppler lidar able to provide global measurements of wind profiles. Aeolus Level‐2B products have been generated and monitored by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in near real‐time since a few weeks after the launch. These products include the horizontal line‐of‐sight (HLOS) winds that are suitable for data assimilation in numerical weather prediction systems. This article presents a series of observing system experiments conducted over summer 2019 to assess the value of the Level‐2B HLOS winds and their impact on the Environment and Climate Change Canada global forecasts. The impact of atmospheric motion vectors (AMVs) on forecasts is also examined and compared with the impact of HLOS winds. Two datasets are used: the HLOS winds produced in near real‐time at ECMWF and those reprocessed later in fall 2020. It is found that the near real‐time data are significantly biased and should be corrected. A look‐up table bias correction based on observation minus background departures is applied to this dataset as initially proposed by ECMWF. The reprocessed data are of better quality and bias corrected using the telescope's primary mirror temperature variations as predictor. The impacts of the near real‐time and reprocessed HLOS winds on forecasts are generally positive for both temperature and wind. The impacts are largest in the troposphere over the Tropics and polar regions. The positive impacts on forecasts are larger with the reprocessed data, particularly in the stratosphere, where a significant degradation over the Southern Hemisphere is found from assimilating the near real‐time data. The normalized forecast error reductions at days 1 and 2 for the wind are ∼1.25% over the Tropics and Southern Hemisphere. The positive impact of the HLOS winds on forecasts is enhanced by ∼40% when the AMVs are not assimilated in the control experiment. The forecast error reduction from assimilating AMVs is, however, two times larger than from assimilating HLOS winds in the extratropics. Conversely, the impact of HLOS winds on forecasts is generally larger in the Tropics.

Consistency and Stability of SNPP ATMS Microwave Observations and COSMIC-2 Radio Occultation over Oceans

Radio occultation (RO) sensor measurements have critical roles in numerical weather prediction (NWP) by complementing microwave and infrared sounder measurements with information of the atmospheric profiles at high accuracy, precision, and vertical resolution. This study evaluates Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) wet temperature and humidity data products’ consistency and stability through inter-comparison with SNPP advanced technology microwave sounder (ATMS) measurements. Through the community radiative transfer model (CRTM), brightness temperature (BT) at SNPP ATMS channels are simulated with COSMIC-2 retrieved atmospheric profiles from two versions of the University Corporation for Atmospheric Research (UCAR) wet profiles (WETprf and WETpf2) as inputs to the CRTM simulation. The analysis was focused on ATMS sounding channels CH07–14 and CH19–22 with sounding weighting function peak heights from 3.2 to 35 km. The COSMIC-2 vs. ATMS inter-comparison indicates that their BT biases are consistent, and the latitudinal difference is <0.3 K over three latitudinal regions. The differences between the two versions of UCAR COSMIC-2 wet profiles are identified and attributed to the differences in the implementation of 1DVAR retrieval algorithms. The stability between UCAR near real-time COSMIC-2 wet profile data and ATMS measurements is also well-maintained. It is demonstrated that the well-sustained quality of COSMIC-2 RO data makes itself a well-suited reference sensor to capture the calibration update of SNPP ATMS. Furthermore, the impacts of the assimilation of COSMIC-2 data into the European Centre for Medium-Range Weather Forecasts (ECMWF) model after 25 March 2020, are evaluated by trending observation-minus-background (O-B) biases, which confirms the statistically significant positive impacts of COSMIC-2 on the ECMWF reanalysis. The validation of stability and consistency between COSMIC-2 and SNPP ATMS ensures the quality of RO and microwave sounder data assimilated into the NWP models.

Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome

Abstract. The impact of using wind observations from the Aeolus satellite in a limited-area numerical weather prediction (NWP) system is being investigated using the limited-area NWP model Harmonie–Arome over the Nordic region. We assimilate the horizontal line-of-sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in September–October 2018 when the satellite was recently launched and a later period in April–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations has degraded between the first and second experiment period over our domain. However, observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods, whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generates flow-dependent analysis increments, with the Aeolus data.

The impact of Aeolus wind retrievals on ECMWF global weather forecasts

AbstractAeolus is the world's first spaceborne Doppler Wind Lidar, providing profiles of horizontal line‐of‐sight (HLOS) wind retrievals. Numerical weather prediction (NWP) impact and error statistics of Aeolus Level‐2B (L2B) wind statistics have been assessed using the European Centre for Medium‐range Weather Forecasts (ECMWF) global data assimilation system. Random and systematic error estimates were derived from observation minus background departure statistics. The HLOS wind random error standard deviation is estimated to be in the range 4.0–7.0 m·s−1 for the Rayleigh‐clear and 2.8–3.6 m·s−1 for the Mie‐cloudy, depending on atmospheric signal levels which in turn depend on instrument performance, atmospheric backscatter properties and the processing algorithms. Complex systematic HLOS wind error variations on time‐scales less than one orbit were identified, most strongly affecting the Rayleigh‐clear winds. NWP departures and instrument housekeeping data confirmed that it is caused by temperature gradients across the primary mirror. A successful bias correction scheme was implemented in the operational processing chain in April 2020. In Observing System Experiments (OSEs), Aeolus provides statistically significant improvement in short‐range forecasts as verified by observations sensitive to temperature, wind and humidity. Longer forecast range verification shows positive impact that is strongest at the day two to three forecast range: ∼2% improvement in root‐mean‐square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere, and polar troposphere. Positive impact up to 9 days is found in the tropical lower stratosphere. Both Rayleigh‐clear and Mie‐cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since 9 January 2020, when Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system, with the Rayleigh‐clear and Mie‐cloudy winds providing similar overall short‐range impact in 2020.

Impact of Assimilating FY-3D MWTS-2 Upper Air Sounding Data on Forecasting Typhoon Lekima (2019)

In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track errors of typhoon Lekima from uses of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances (EXP_AD) with those from FY-3D MWTS-2 upper-air sounding data at channels 5–7 (EXP_AMD). The clear-sky mean bias-corrected observation-minus-background (O-B) values of FY-3D MWTS-2 channels 5, 6, and 7 are 0.27, 0.10 and 0.57 K, respectively, which are smaller than those without bias corrections. Compared with the control experiment, which was the forecast of the WRF model without use of satellite data, the assimilation of satellite radiances can improve the forecast performance and reduce the mean track error by 8.7% (~18.4 km) and 30% (~58.6 km) beyond 36 h through the EXP_AD and EXP_AMD, respectively. The direction of simulated steering flow changed from southwest in the EXP_AD to southeast in the EXP_AMD, which can be pivotal to forecasting the landfall of typhoon Lekima (2019) three days in advance. Assimilation of MWTS-2 upper-troposphere channels 5–7 has great potential to improve the track forecasts for typhoon Lekima.

Impact of assimilating FY-3C MWHS2 data in the RMAPS-ST forecast system on its rainfall forecasts

In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder (MWHS2) data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term (RMAPS-ST) operational system, which is developed by the Institute of Urban Meteorology of the China Meteorological Administration, four experiments were carried out in this study: (i) Coldstart (no observations assimilated); (ii) CON (assimilation of conventional observations); (iii) FY3 (assimilation of FY-3C MWHS2 only); and (iv) FY3+CON (simultaneous assimilation of FY-3C MWHS2 and conventional observations). A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study. When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system, data quality control and bias correction were performed so that the O-B (observation minus background) values of the five humidity channels of MWHS2 became closer to a normal distribution, and the data basically satisfied the unbiased assumption. The results showed that, in this case, the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments. Meanwhile, the prediction of atmospheric parameters for the mesoscale field was also improved, and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced. This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.摘要 中层水汽初值的误差是数值预报, 尤其是短期预报不确定性的重要来源. 星载微波湿度计资料能很好地弥补常规资料的不足, 同化微波湿度计资料对改善对流层中高层的水汽初始场精度有着重要意义. 本研究基于新一代快速更新多尺度资料分析和预报系统短期数值预报系统 (RMAPS-ST), 选取 2019 年6月 4–6 日中国华中地区降水过程开展了 FY-3C MWHS2 资料同化对于此次降水预报的影响评估. 研究开展了4组试验: 冷启动试验, 控制试验, 同化 FY-3C MWHS 试验, 同时同化常规观测和 FY-3C MWHS 试验. 结果表明:经质量控制及偏差订正之后 FY-3C MWHS2 各通道 O-B 明显趋于正态分布, 同化 FY-3C MWHS 资料后, 对此次降水过程降水落区和强度的预报均优于控制试验和冷启动试验, 同时对于环境场的预报也有改善.

Evaluation of aqua MODIS thermal emissive bands stability through radiative transfer modeling

Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua has been in operation providing continuous global observations for science research and applications since 2002. The long-term stability of thermal emissive bands (TEBs) of Aqua MODIS was monitored through inter-comparisons with measurements by hyperspectral or multi-spectral infrared sensors or through vicarious monitoring over cold targets such as Dome-C and deep convective clouds. The radiative transfer modeling (RTM)-based simulation models are developed to perform the long-term monitoring of the stability of Aqua MODIS TEBs. Long-term European Centre for Medium-Range Weather Forecasts global atmospheric reanalysis data are used as inputs to the RTM simulation. By confining ocean in low to middle latitudes as the area of interest, the long-term stabilities of Aqua MODIS TEBs are monitored through observation minus background (O-B) brightness temperature (BT) bias between MODIS measurements and simulations from the RTM. In general, the RTM-based O-B analysis shows that the long-term stability of Aqua MODIS TEBs are maintained well. It is shown that Aqua MODIS surface bands are all radiometrically stable with yearly BT bias drifts <0.005 K / year for B20, B22, and B23 and ∼0.01 K / year for B31 and B32. The carbon dioxide (CO2) absorption channels of Aqua MODIS, e.g., B33 to B36, are stable with a BT bias drift <0.005 K / year. It is also found that after accounting for the long-term growth of greenhouse gas N2O, the O-B bias trends for B24 and B25 are quite stable with yearly BT bias drifts around 0.0002 and 0.0055 K / year, respectively. The stabilities of the moisture-sensitive channels B27 to B29 and the ozone channel B30 of Aqua MODIS are also evaluated and the remnant small variations in the O-B bias trending of these channels are further discussed. The RTM-based inter-comparison of MODIS TEB measurements with global atmospheric and climate re-analysis data provides validation of the long-term radiometric stability of Aqua MODIS TEBs. The analysis in this paper also demonstrates that the comparison of well-calibrated MODIS TEB measurements with RTM simulation helps quantify the long-term impacts of global greenhouse gas concentration growth on the BT decreases in the MODIS greenhouse gas-sensitive channels.