Wind Speed Bias Research Articles

Intercalibration of Backscatter Measurements among Ku-Band Scatterometers Onboard the Chinese HY-2 Satellite Constellation

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.

Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

Abstract. Measure–correlate–predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on-site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on-site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared with regard to accuracy in mean, variance, and turbine energy production – namely, variance ratio (VR) and linear regression with residuals (LR). Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. All these reanalysis data sets showed an overpronounced annual course of wind speed (i.e., wind speeds too high in winter and too low in summer). However, despite the mathematical similarity of the two MCP methods, these errors in the data resulted in very different seasonal biases when either the VR or LR methods were used for the MCP calculations. In general, the VR method produced overestimations of the mean wind speed when measuring in summer and underestimations in the case of winter measurements. The LR method, in contrast, predominantly led to opposite results. An analysis of the bias in variance did not show such a clear seasonal variation. Overall, the variance error plays only a minor role for the accuracy in energy compared to the error in mean wind speed. Besides the experimental analysis, a theoretical framework is presented which explains these phenomena. This framework enables us to trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. In summary, three aspects are identified as the main influential factors for the seasonal biases in mean wind speed: (1) the (dis-)similarity of the real wind conditions on-site in correlation and correction period (representativeness of the measurement period), (2) the capability of the reference data to reproduce the seasonal course of wind speed, and (3) the regression parameter β1 (slope) of the linear MCP method. This theoretical framework can also be considered valid for different measurement durations, other reference data sets, and other regions of the world.

Doppler-Lidar Evaluation of HRRR-Model Skill at Simulating Summertime Wind Regimes in the Columbia River Basin during WFIP2

AbstractComplex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s−1 and RMSEs of 2-3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.

A new perspective on evaluating high-resolution urban climate simulation with urban canopy parameters

The ‘state-of-the-art’ urban climate models have not been evaluated against dense meteorological networks under various weather conditions. In this study, we conducted high-resolution urban climate simulations in Beijing and investigated the relationship between their performances and urban canopy parameters (UCPs). The latest version of single-layer (UCM) and multi-layer (BEP) urban canopy models were tested separately. Results show that model performances are insensitive to rainfall events in summer, but change significantly with wind conditions in winter. Inclusion of UCPs has a limited impact on air temperature simulations. In terms of wind speed simulations, consistent overestimations by UCM and BEP are found in both summer and winter. The overestimation by BEP reduces significantly when UCPs are available, especially on windy days in winter. On the other hand, performance of UCM can degrade over urban areas with canopy parameters. Wind speed biases are found to correlate significantly with UCPs. The accuracy of wind speed simulations by UCM and BEP increases with urban fraction and building surface ratio. This indicates poor model performances over low-density urban areas, which requires enhanced aerodynamic parameterizations to better account for UCPs. This study reveals the limitation of current urban climate simulations and provides guidance for future model developments.

Validation of NOAA CyGNSS Wind Speed Product with the CCMP Data

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CyGNSS) mission was launched in December 2016, which can remotely sense sea surface wind with a relatively high spatio-temporal resolution for tracking tropical cyclones. In recent years, with the gradual development of the geophysical model function (GMF) for CyGNSS wind retrieval, different versions of CyGNSS Level 2 products have been released and their performance has gradually improved. This paper presents a comprehensive evaluation of CyGNSS wind product v1.1 produced by the National Oceanic and Atmospheric Administration (NOAA). The Cross-Calibrated Multi-Platform (CCMP) analysis wind (v02.0 and v02.1 near real time) products produced by Remote Sensing Systems (RSS) were used as the reference. Data pairs between the NOAA CyGNSS and RSS CCMP products were processed and evaluated by the bias and standard deviation SD. The CyGNSS dataset covers the period between May 2017 and December 2020. The statistical comparisons show that the bias and SD of CyGNSS relative to CCMP-nonzero collocations when the flag of CCMP winds is nonzero are –0.05 m/s and 1.19 m/s, respectively. The probability density function (PDF) of the CyGNSS winds coincides with that of CCMP-nonzero. Furthermore, the average monthly bias and SD show that CyGNSS wind is consistent and reliable generally. We found that negative deviation mainly appears at high latitudes in both hemispheres. Positive deviation appears in the China Sea, the Arabian Sea, and the west of Africa and South America. Spatial–temporal analysis demonstrates the geographical anomalies in the bias and SD of the CyGNSS winds, confirming that the wind speed bias shows a temporal dependency. The verification and comparison show that the remotely sensed wind speed measurements from NOAA CyGNSS wind product v1.1 are in good agreement with CCMP winds.

Prediction of atmospheric motion vectors around typhoons using generative adversarial network

In this study, atmospheric motion vectors (AMVs) were derived from the satellite images predicted using a generative adversarial network (GAN) and a deep multi-scale frame prediction algorithm. The GAN was trained and tested with a sequence of the satellite images of a COMS satellite infrared-window channel under the 68 tropical cyclones. The inputs of the consecutive satellite images with 15-min interval were then processed using the trained GAN model to generate satellite images in the next time steps. To further enhance the model’s predictability, particle image velocimetry based on the theory of cross-correlation schemes was employed to the GAN-generated satellite image sequence and AMVs were produced. The GAN-derived AMVs were validated with the wind fields based on the numerical weather prediction (NWP) and radiosonde observations. The comparisons showed that the GAN-derived AMVs depicted the structure of atmospheric circulations with a certain level of accuracy. Through comparison with the radiosonde observations, the root-mean-square error and the wind speed bias of the GAN-derived AMVs were comparable to, and even smaller than those of the NWP-derived wind fields. The current approach may enhance the accuracy in predicting short-term wind velocity fields, which in turn may provide more realistic inputs in storm surge modeling.

Towards accurate and practical drone-based wind measurements with an ultrasonic anemometer

Abstract. Wind data collection in the atmospheric boundary layer benefits from short-term wind speed measurements using unmanned aerial vehicles. Fixed-wing and rotary-wing devices with diverse anemometer technology have been used in the past to provide such data, but the accuracy still has the potential to be increased. A lightweight drone for carrying an industry-standard precision sonic anemometer was developed. Accuracy tests have been performed with the isolated anemometer at high tilt angles in a calibration wind tunnel, with the drone flying in a large wind tunnel and with the full system flying at different heights next to a bistatic lidar reference. The propeller-induced flow deflects the air to some extent, but this effect is compensated effectively. The data fusion shows a substantial reduction of crosstalk (factor of 13) between ground speed and wind speed. When compared with the bistatic lidar in very turbulent conditions, with a 10 s averaging interval and with the unmanned aerial vehicle (UAV) constantly circling around the measurement volume of the lidar reference, wind speed measurements have a bias between −2.0 % and 4.2 % (root-mean-square error (RMSE) of 4.3 % to 15.5 %), vertical wind speed bias is between −0.05 and 0.07 m s−1 (RMSE of 0.15 to 0.4 m s−1), elevation bias is between −1 and 0.7∘ (RMSE of 1.2 to 6.3∘), and azimuth bias is between −2.6 and 7.2∘ (RMSE of 2.6 to 8.0∘). Key requirements for good accuracy under challenging and dynamic conditions are the use of a full-size sonic anemometer, a large distance between anemometer and propellers, and a suitable algorithm for reducing the effect of propeller-induced flow. The system was finally flown in the wake of a wind turbine, successfully measuring the spatial velocity deficit and downwash distribution during forward flight, yielding results that are in very close agreement to lidar measurements and the theoretical distribution. We believe that the results presented in this paper can provide important information for designing flying systems for precise air speed measurements either for short duration at multiple locations (battery powered) or for long duration at a single location (power supplied via cable). UAVs that are able to accurately measure three-dimensional wind might be used as a cost-effective and flexible addition to measurement masts and lidar scans.

Drivers of Air‐Sea CO2 Flux Seasonality and its Long‐Term Changes in the NASA‐GISS Model CMIP6 Submission

AbstractClimate change will affect both the mean state and seasonality of marine physical and biogeochemical properties, with important implications for the oceanic sink of atmospheric CO2. Here, we investigate the seasonal cycle of the air‐sea exchange of CO2 and pCO2,sw (surface seawater pCO2) and their long term changes using the CMIP6 submission of the NASA‐GISS modelE (GISS‐E2.1‐G). In comparison to the CMIP5 submission (GISS‐E2‐R), we find that on the global scale, the seasonal cycles of the CO2 flux and NPP have improved, while the seasonal cycles of dissolved inorganic carbon (DIC), alkalinity, and macronutrients have deteriorated. Moreover, for all ocean biogeochemistry fields, changes in skill between E2.1‐G and E2‐R display large regional variability. For E2.1‐G, we find similar modeled and observed CO2 flux seasonal cycles in the subtropical gyres, where seasonal anomalies of pCO2,sw and the flux are temperature‐driven, and the Southern Ocean, where anomalies are DIC‐driven. Biases in these seasonal cycles are largest in the subpolar and equatorial regions, driven by a combination of biases in temperature, DIC, alkalinity, and wind speed. When comparing the historical simulation to a simulation with an idealized increase in atmospheric pCO2, we find that the seasonal amplitudes of the CO2 flux and pCO2,sw generally increase. These changes are produced by increases in the sensitivity of pCO2,sw to its respective drivers. These findings are consistent with the notion that the seasonality of pCO2,sw is expected to increase due to the increase of atmospheric pCO2, with changes in the seasonality of temperature, DIC, and alkalinity having secondary influences.

Analysis of the High-Latitude Sea Surface Wind Acquisition Ability of Seven Satellite Scatterometers

Seven satellite scatterometers, namely, the C-band MetOp-A/Advanced Scatterometer (ASCAT), MetOp-B/ASCAT, MetOp-C/ASCAT, Ku-band HY-2A/SCAT, HY-2B/SCAT, Chinese-French Oceanography Satellite (CFOSAT)/SCAT, and SCATSAT-1/OceanSat Satellite Scatterometer (OSCAT2), are operating in orbits. These satellites make a high-frequency observation of sea surface winds possible, particularly at high latitudes. This work analyzes the passing time, passing frequency, coverage of these seven scatterometers, and the quality of high-latitude surface wind products. All the scatterometer wind products were reproduced with the same processing procedures, in terms of backscatter calibration, wind retrieval, numerical weather prediction (NWP) wind, and quality control. The target region (74–78 N, 167°–171 W) was chosen to analyze the passing frequency. A spatial grid of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.25^{\circ } \times 0$ </tex-math></inline-formula> .25° in the range of 60° N–88° N was chosen to analyze the spatial coverage of the scatterometers. The results show that more than 40 observation times can be provided daily by all the seven scatterometers; however, there is a 9-h gap between UTC 9:00 and UTC 18:00, suggesting that international cooperation is needed for optimizing the equatorial crossing time in future scatterometer missions, such that an optimal virtual scatterometer constellation can be achieved. The scatterometer winds are compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) winds, and with each other. The comparison results confirmed noticeable wind speed biases due to sea surface temperature (SST) in all Ku-band scatterometer winds with respect to the C-band scatterometer and ERA5 winds.

Validation of Doppler Wind Lidar during Super Typhoon Lekima (2019)

This study undertook verification of the applicability and accuracy of wind data measured using a WindCube V2 Doppler Wind Lidar (DWL). The data were collected as part of a field experiment in Zhoushan, Zhejiang Province (China), which was conducted by Shanghai Typhoon Institute of China Meteorological Administration during the passage of Super Typhoon Lekima (2019). The DWL measurements were compared with balloon-borne GPS radiosonde (GPS sonde) data, which were acquired using balloons launched from the DWL location. Results showed that wind speed measured by GPS sonde at heights of 100 m) was found for DWL-measured wind speed time-averaged during the ascent of the GPS sonde from the ground surface to the height of 270 m (correlation coefficient: 0.82; root mean square (RMS): 2.19). Analysis revealed that precipitation intensity (PI) exerts considerable influence on both the carrier-to-noise ratio and the rate of missing DWL data; however, PI has minimal effect on the wind speed bias of DWL measurements. Specifically, the rate of missing DWL data increased with increasing measurement height and PI. For PI classed as heavy rain or less (PI 90 mm·h−1), only data below 100 m were valid. Up to the height of 300 m, the RMS of the DWL measurements was nearly half that of wind profile radar (WPR) estimates (4.32 m·s−1), indicating that DWL wind data are more accurate than WPR data under typhoon conditions.

CYGNSS Ocean Surface Wind Validation in the Tropics

AbstractSurface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass, and momentum across Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near-surface wind speed over the tropical and subtropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s−1 root-mean-square difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air–sea interaction in regions that are relatively undersampled by other observing platforms.

Dutch Offshore Wind Atlas Validation against Cabauw Meteomast Wind Measurements

The Dutch Offshore Wind Atlas (DOWA) is validated against wind speed and direction measurements from the Cabauw meteorological mast for a 10-year period and at heights between 10 m and 200 m. The validation results are compared to the Royal Netherlands Meteorological Institute (KNMI) North Sea Wind (KNW) atlas. It is found that the average difference (bias) between DOWA wind speeds and those measured at Cabauw varies for the different heights between −0.1 m/s to 0.3 m/s. Significant differences between DOWA and KNW are only found at altitudes of 10 m and 20 m, where KNW performs better. For heights above 20 m, there is no significant difference between DOWA and KNW with respect to the 10-year averaged wind speed bias. The diurnal cycle is better captured by DOWA compared to KNW, and the hourly correlation is slightly improved. In addition, a comparison with the global European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and ERA5 reanalyses (used for KNW and DOWA, respectively) is made, highlighting the added skill provided by downscaling those global datasets with the weather model HARMONIE.

Wind speed inversion and in-orbit assessment of the imaging altimeter on Tiangong-2 space station

Imaging altimeter (IALT) is a new type of radar altimeter system. In contrast to the conventional nadir-looking altimeters, such as HY-2A altimeter, Jason-1/2, and TOPEX/Poseidon, IALT observes the earth surface at low incident angles (2.5°–8°), so its swath is much wider and its spatial resolution is much higher than the previous altimeters. This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station. Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles, a neural network is used to retrieve the ocean surface wind speed in this study. The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed, buoy wind speed, and in-situ ship measurements. The results show that the retrieved wind speed bias is about −0.21 m/s, and the root-mean-square (RMS) error is about 1.85 m/s. The wind speed accuracy of IALT meets the performance requirement.

Improved Prediction of Cold-Air Pools in the Weather Research and Forecasting Model Using a Truly Horizontal Diffusion Scheme for Potential Temperature

AbstractThe terrain-following vertical coordinate system used by many atmospheric models, including the Weather Research and Forecasting (WRF) Model, is prone to errors in regions of complex terrain. These errors stem, in part, from the calculation of horizontal gradients within the diffusion term of the momentum or scalar evolution equations. In WRF, such gradients can be calculated along coordinate surfaces, or using metric terms that help account for grid skewness. However, neither of these options ensures a truly horizontal gradient calculation, especially if a grid cell is skewed enough that the heights of the neighboring grid points used in the calculation fall outside the vertical range of the cell. In this work, an improved scheme that uses Taylor series approximations to vertically interpolate variables to the level necessary for a truly horizontal gradient calculation is implemented in WRF for the diffusion of potential temperature. The scheme is validated using an atmosphere-at-rest configuration, in which spurious flows develop only as a result of numerical errors and can thus be used as a proxy for model performance. Following validation, the method is applied to the simulation of cold-air pools (CAPs), which occur in regions of complex terrain and are characterized by strong near-surface temperature gradients. Using the truly horizontal scheme, idealized simulations demonstrate reduced numerical mixing in a quiescent CAP, and a realistic case study in the Columbia River basin shows a reduction in positive wind speed bias by up to roughly 20% compared to observations from the Second Wind Forecast Improvement Project.

Assimilation of Satellite-Derived Soil Moisture for Improved Forecasts of the Great Plains Low-Level Jet

AbstractIn the context of forecasting societally impactful Great Plains low-level jets (GPLLJs), the potential added value of satellite soil moisture (SM) data assimilation (DA) is high. GPLLJs are both sensitive to regional soil moisture gradients and frequent drivers of severe weather, including mesoscale convective systems. An untested hypothesis is that SM DA is more effective in forecasts of weakly synoptically forced, or uncoupled GPLLJs, than in forecasts of cyclone-induced coupled GPLLJs. Using the NASA Unified Weather Research and Forecasting (NU-WRF) Model, 75 GPLLJs are simulated at 9-km resolution both with and without NASA Soil Moisture Active Passive SM DA. Differences in modeled SM, surface sensible (SH) and latent heat (LH) fluxes, 2-m temperature (T2), 2-m humidity (Q2), PBL height (PBLH), and 850-hPa wind speed (W850) are quantified for individual jets and jet-type event subsets over the south-central Great Plains, as well as separately for each GPLLJ sector (entrance, core, and exit). At the GPLLJ core, DA-related changes of up to 5.4 kg m−2in SM can result in T2, Q2, LH, SH, PBLH, and W850 differences of 0.68°C, 0.71 g kg−2, 59.9 W m−2, 52.4 W m−2, 240 m, and 4 m s−1, respectively. W850 differences focus along the jet axis and tend to increase from south to north. Jet-type differences are most evident at the GPLLJ exit where DA increases and decreases W850 in uncoupled and coupled GPLLJs, respectively. Data assimilation marginally reduces negative wind speed bias for all jets, but the correction is greater for uncoupled GPLLJs, as hypothesized.

The Making of the New European Wind Atlas – Part 2: Production and evaluation

Abstract. This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the downscaling using WAsP. We show the main results from the final wind atlas and present a comprehensive evaluation of each component of the NEWA model chain using observations from a large set of tall masts located all over Europe. The added value of the WRF and WAsP downscaling of wind climatologies is evaluated relative to the performance of the driving ERA5 reanalysis and shows that the WRF downscaling reduces the mean wind speed bias and spread relative to that of ERA5 from -1.50±1.30 to 0.02±0.78 m s−1. The WAsP downscaling has an added positive impact relative to that of the WRF model in simple terrain. In complex terrain, where the assumptions of the linearized flow model break down, both the mean bias and spread in wind speed are worse than those from the raw mesoscale results.

Observation of Jet Stream Winds during NAWDEX and Characterization of Systematic Meteorological Analysis Errors

Abstract Observations across the North Atlantic jet stream with high vertical resolution are used to explore the structure of the jet stream, including the sharpness of vertical wind shear changes across the tropopause and the wind speed. Data were obtained during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) by an airborne Doppler wind lidar, dropsondes, and a ground-based stratosphere–troposphere radar. During the campaign, small wind speed biases throughout the troposphere and lower stratosphere of only −0.41 and −0.15 m s−1 are found, respectively, in the ECMWF and Met Office analyses and short-term forecasts. However, this study finds large and spatially coherent wind errors up to ±10 m s−1 for individual cases, with the strongest errors occurring above the tropopause in upper-level ridges. ECMWF and Met Office analyses indicate similar spatial structures in wind errors, even though their forecast models and data assimilation schemes differ greatly. The assimilation of operational observational data brings the analyses closer to the independent verifying observations, but it cannot fully compensate for the forecast error. Models tend to underestimate the peak jet stream wind, the vertical wind shear (by a factor of 2–5), and the abruptness of the change in wind shear across the tropopause, which is a major contribution to the meridional potential vorticity gradient. The differences are large enough to influence forecasts of Rossby wave disturbances to the jet stream with an anticipated effect on weather forecast skill even on large scales.

Verification of the regional atmospheric model CCLM v5.0 with conventional data and lidar measurements in Antarctica

Abstract. The nonhydrostatic regional climate model CCLM was used for a long-term hindcast run (2002–2016) for the Weddell Sea region with resolutions of 15 and 5 km and two different turbulence parametrizations. CCLM was nested in ERA-Interim data and used in forecast mode (suite of consecutive 30 h long simulations with 6 h spin-up). We prescribed the sea ice concentration from satellite data and used a thermodynamic sea ice model. The performance of the model was evaluated in terms of temperature and wind using data from Antarctic stations, automatic weather stations (AWSs), an operational forecast model and reanalyses data, and lidar wind profiles. For the reference run we found a warm bias for the near-surface temperature over the Antarctic Plateau. This bias was removed in the second run by adjusting the turbulence parametrization, which results in a more realistic representation of the surface inversion over the plateau but resulted in a negative bias for some coastal regions. A comparison with measurements over the sea ice of the Weddell Sea by three AWS buoys for 1 year showed small biases for temperature around ±1 K and for wind speed of 1 m s−1. Comparisons of radio soundings showed a model bias around 0 and a RMSE of 1–2 K for temperature and 3–4 m s−1 for wind speed. The comparison of CCLM simulations at resolutions down to 1 km with wind data from Doppler lidar measurements during December 2015 and January 2016 yielded almost no bias in wind speed and a RMSE of ca. 2 m s−1. Overall CCLM shows a good representation of temperature and wind for the Weddell Sea region. Based on these encouraging results, CCLM at high resolution will be used for the investigation of the regional climate in the Antarctic and atmosphere–ice–ocean interactions processes in a forthcoming study.

Temporal variability of GNSS-Reflectometry ocean wind speed retrieval performance during the UK TechDemoSat-1 mission

This paper presents the temporal evolution of Global Navigation Satellite System Reflectometry (GNSS-R) ocean wind speed retrieval performance during three years of the UK TechDemoSat-1 (TDS-1) mission. TDS-1 was launched in July 2014 and provides globally distributed spaceborne GNSS-R data over a lifespan of over three years, including several months of 24/7 operations. TDS-1 wind speeds are computed using the NOC Calibrated Bistatic Radar Equation algorithm version 0.5 (C-BRE v0.5), and are evaluated against ERA5 high resolution re-analysis data over the period 2015–2018. Analyses reveal significant temporal variability in TDS-1 monthly wind speed retrieval performance over the three years, with the best performance (~2 m∙s−1) achieved in the early part of the mission (May 2015). The temporal variability of retrieval performance is found to be driven by several non-geophysical factors, including TDS-1 platform attitude uncertainty and spatial/temporal changes in GPS transmit power from certain satellites. Evidence is presented of the impact of the GPS Block IIF Flex mode on retrieved GNSS-R wind speed after January 2017, which results in significantly underestimated ocean winds over a large region covering the North Atlantic, northern Indian Ocean, the Mediterranean, the Black Sea, and the Sea of Okhotsk. These GPS transmit power changes are shown to induce large negative wind speed biases of up to 3 m∙s−1. Analyses are also presented of the sensitivity of TDS-1 wind speed retrieval to platform attitude uncertainty using statistical simulations. It is suggested that a 4° increase in attitude uncertainty can produce up to 1 m∙s−1 increase in RMSE, and that TDS-1 attitude data do not fully reflect actual platform attitude. We conclude that the lack of knowledge about the GNSS-R nadir antenna gain map and TDS-1 platform-attitude limits the ability to determine the achievable wind speed retrieval performance with GNSS-R on TDS-1. The paper provides recommendations that accurate attitude knowledge and a good characterisation of GNSS-R nadir antenna patterns should be prioritised for future GNSS-R missions.

Assessment of INSAT-3D/INSAT-3DR Derived Atmospheric Motion Vectors Using Wind Profiler Observations Located at Gadanki, India

The accurate estimation of atmospheric motion vectors (AMVs) derived using sequences of images from geostationary satellites is very important because of their extensive use in improving initial conditions of the numerical weather prediction (NWP) models. To assess the quality of retrieved AMVs, validation of these winds with ground-based observation is very important. In the present study, the evaluation of AMVs retrieved using the data from geostationary satellites, viz. INSAT-3D/INSAT-3DR, has been carried out with respect to wind profiler data and radiosonde data. The wind profiler used in the present study is an L-band radar lower atmosphere wind profiler and is installed at the National Atmospheric Research Laboratory, Gadanki, located at 13.27° N, 79.10° E. This instrument measures continuous high-resolution wind measurements in the lower atmosphere. The radiosonde data available near Gadanki are used for validation. In this study, normal infrared (IR) AMV derived using 10.5–11.5 μm channel of INSAT-3D and INSAT-3DR satellites and staggering-mode IR AMV retrieved using INSAT-3D/INSAT-3DR combination are used. AMV accuracy assessment was performed for the years 2017 and 2018 by calculating statistical scores, viz. root mean square vector difference, wind speed bias and a number of matching points. In addition, the INSAT-3D/INSAT-3DR AMV and profiler wind data are also used to study the characteristics of low-level jet during active and break period of the Indian summer monsoon of 2017 and 2018. The accuracy assessment through this study shows the usefulness of AMVs for improving the initial conditions in NWP models in future.