Numerical Weather Prediction Applications Research Articles

Evaluation of an In‐Canopy Wind and Wind Adjustment Factor Model for Wildfire Spread Applications Across Scales

AbstractThe representation of vegetative sub‐canopy wind is critical in numerical weather prediction (NWP) models for the determination of the air‐surface exchange processes of heat, momentum, and trace gases. Because of the relationship between wind speed and fire behaviors, the influence of the canopy on near‐surface wind speed is critical for prognostic fire spread models used in regional NWP models. In practice, the wind speed at the midflame point of fires (midflame wind speed) is used to determine the rate of fire spread. However, the wind speeds from most in situ measurements and NWP models are taken at some reference height above the canopy and fire flames. Hence, this study develops a modular and computationally‐efficient one‐dimensional model set composed of a canopy wind model and a wind adjustment factor (WAF) model for NWP applications across scales. The model set uses prescribed foliage shape functions to represent the vertical vegetation profile and its impacts on the three‐dimensional structure of horizontal wind speeds. Results from the canopy wind model well agree with ground‐based observations with average mean absolute bias, root mean square error and determination coefficients around 0.18 m s−1, 0.40 m s−1and 0.90, respectively. The WAF model provides midflame wind speeds by estimating the WAF based on canopy, fire and flame characteristics. Various user‐definable options provide flexibility to adapt to variations in canopy characteristics and additional complexities associated with wildfires. The model set is expected to improve NWP models by providing an improved representation of the sub‐grid wind flows at any spatial scale.

Performance of the Atmospheric Radiative Transfer Simulator (ARTS) in the 600–1650 cm−1 Region

The Atmospheric Radiative Transfer Simulator (ARTS) has been widely used in the radiation transfer simulation from microwave to terahertz. Due to the same physical principles, ARTS can also be used for simulations of thermal infrared (TIR). However, thorough evaluations of ARTS in the TIR region are still lacking. Here, we evaluated the performance of ARTS in 600–1650 cm−1 taking the Line-By-Line Radiative Transfer Model (LBLRTM) as a reference model. Additionally, the moderate resolution atmospheric transmission (MODTRAN) band model (BM) and correlated-k (CK) methods were also used for comparison. The comparison results on the 0.001 cm−1 spectral grid showed a high agreement (sub-0.1 K) between ARTS and LBLRTM, while the mean bias difference (MBD) and root mean square difference (RMSD) were less than 0.05 K and 0.3 K, respectively. After convolving with the spectral response functions of the Atmospheric Infra-Red Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the brightness temperature (BT) differences between ARTS and LBLRTM became smaller with RMSDs of <0.1 K. The comparison results for Jacobians showed that the Jacobians calculated by ARTS and LBLRTM were close for temperature (can be used for Numerical Weather Prediction application) and O3 (excellent Jacobian fit). For the water vapor Jacobian, the Jacobian difference increased with an increasing water vapor content. However, at extremely low water vapor values (0.016 ppmv in this study), LBLRTM exhibited non-physical mutations, while ARTS was smooth. This study aims to help users understand the simulation accuracy of ARTS in the TIR region and the improvement of ARTS via the community.

Direct assimilation of AVHRR satellite radiance measurements in a reanalysis system

AbstractThe Advanced Very‐High‐Resolution Radiometer (AVHRR) is a broad‐band, five‐channel scanner sensing in the visible, near‐infrared and thermal infrared portions of the electromagnetic spectrum. AVHRR instruments onboard polar‐orbiting satellites have data records spanning 30 years. The radiances from the infrared channels of AVHRR have been directly assimilated over the ice‐free ocean in the NASA Goddard Earth Observing System (GEOS) since 2017 to constrain skin sea surface temperature (SST). The GEOS system already uses an advanced bulk SST and a skin SST model which incorporates diurnal warming and cool‐skin temperature on the surface of the ocean. The positive contribution of this effort to the Numerical Weather Prediction (NWP) makes it desirable to extend the skin SST data assimilation procedure to reanalysis applications. The AVHRR data from platforms that were previously unexplored for NWP applications are assimilated at the Global Modeling and Assimilation Office (GMAO) at NASA, for an upcoming reanalysis project. This study uses results from reanalysis experiments to assess the impact of assimilating AVHRR radiances over open ocean on the atmospheric state variables, focusing on the analyzed skin temperatures that include the active skin SST model. It is demonstrated that including the skin temperature variations in atmospheric analysis is generally beneficial. The addition of radiance measurements from AVHRR provides further improvements to the overall reanalysis system performance. by helping to correct the diurnal heating and reducing errors in the background departure of hyperspectral radiance observations, which are an essential component of atmospheric reanalyses.

The Common Community Physics Package (CCPP) Framework v6

Abstract. The Common Community Physics Package (CCPP) is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model's dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and experimentation with physics–dynamics coupling methods while simultaneously offering capabilities for use in numerical weather prediction (NWP) operations. The CCPP Framework supports configurations ranging from process studies to operational NWP as it enables host models to assemble the parameterizations in flexible suites. Framework capabilities include variability in scheme call order; ability to group parameterizations for calls in different parts of the host model, allowing intervening computation or coupling to additional components; options to call some parameterizations more often than others; and automatic variable transformations. The CCPP Framework was developed by the Developmental Testbed Center and is distributed with a single-column model that can be used to test innovations and to conduct hierarchical studies in which physics and dynamics are decoupled. It is also an integral part of the Unified Forecast System, a community-based, coupled, comprehensive Earth modeling system designed to support research and be the source system for the NOAA's operational NWP applications. Finally, the CCPP Framework is under various stages of adoption by a number of other models in the wider community.

Applications of Geostationary Hyperspectral Infrared Sounder Observations: Progress, Challenges, and Future Perspectives

Abstract A hyperspectral infrared (IR) sounder from geostationary orbit provides nearly continuous measurements of atmospheric thermodynamic and dynamic information within a weather cube, specifically the atmospheric temperature, moisture, and wind information at different pressure levels that are critical for improving high-impact weather (HIW) nowcasting and numerical weather prediction (NWP). Geostationary hyperspectral IR sounders (GeoHIS) have been on board China’s Fengyun-4 series since 2016 and will be on board Europe’s Meteosat Third Generation (MTG) series in the 2024 time frame; the United States and other countries are also planning to include GeoHIS instruments on their next generation of geostationary weather satellites. Although availability of on-orbit GeoHIS data are limited currently, studies have been conducted and progress has been made on developing the applications of high-temporal-resolution GeoHIS observations. These include but are not limited to deriving three-dimensional wind fields for nowcasting and NWP applications, trending atmospheric instability for warning in preconvective environments, conducting impact studies with data from the experimental Geostationary Interferometric Infrared Sounder (GIIRS) on board Fengyun-4A, preparing observing system simulation experiments (OSSEs), and monitoring diurnal variation of atmospheric composition. This paper provides an overview of the current applications of GeoHIS, discusses the data processing challenges, and provides perspectives on future development. The purpose is to provide direction on utilization of the current and assist preparation for the upcoming GeoHIS observations for nowcasting, NWP and other applications.

Dependency of Mesoscale Organization on Grid Anisotropy in Large‐Eddy Simulations of Convective Boundary Layers at Gray Zone Resolutions

AbstractA new generation of operational atmospheric models operating at horizontal resolutions in the range 200 m ∼ 2 km is becoming increasingly popular for operational use in numerical weather prediction and climate applications. Such grid spacings are becoming sufficiently fine to resolve a fraction of the turbulent transports. Here we analyze Large‐eddy simulation results of a convective boundary layer obtained by coarsening horizontal grid spacings up to 800 m. The aim is to explore the dependency of the mean state and turbulent fluxes on the grid resolution. Both isotropic and anisotropic eddy diffusion approaches are evaluated, where in the latter case the horizontal and vertical eddy diffusivities differ in accord with their horizontal and vertical grid spacings. For coarsening horizontal grid sizes entrainment at the top of the boundary layer tends to get slightly enhanced for isotropic diffusion, whereas for the anisotropic diffusion approach the vertically well‐mixed boundary‐layer structure becomes severely degraded. An analysis of the energy spectrum shows that anisotropic diffusion causes relatively more dissipation of variance at smaller length scales. This leads, in turn, to a shift of spectral energy toward larger length scales that also becomes apparent from a rather different kind of spatial organization of convection. The present study therefore suggests that details with regards to the representation of processes at small scales might impact the organization at length scales much larger than the smallest scales that can be resolved by the model.

MetOp/AVHRR Snow Detection Method for Meteorological Applications

AbstractSnow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It also has far-reaching effects on ecosystems of snow-covered areas. Therefore, global snow-cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow-cover information that is suitable for these needs. Highly variable surface and snow-cover features suggest that operational snow extent algorithms may benefit from at least a partly empirical approach that is based on carefully analyzed training data. Here, a new two-phase snow-cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) on board the MetOp satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the MetOp/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud masking. Pixels that cannot be classified reliably to snow or snow-free, because of clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow-depth and state-of-the-ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data that may be suitable for numerical weather prediction, hydrological modeling, and other applications.

Wildfire Pyroconvection and CAPE: Buoyancy’s Drying and Atmospheric Intensification—Fort McMurray

The accurate prediction of wildfire behavior and spread is possible only when fire and atmosphere simulations are coupled. In this work, we present a mechanism that causes a small fire to intensify by altering the atmosphere. These alterations are caused by fire-related fluxes at the surface. The fire plume and fluxes increase the convective available potential energy (CAPE) and the chance of the development of a strong pyroconvection system. To study this possible mechanism, we used WRF-Fire to capture fire line propagation as the result of interactions between heat and moisture fluxes, pressure perturbations, wind shear development and dry air downdraft. The wind patterns and dynamics of the pyroconvection system are simulated for the Horse River wildfire at Fort McMurray, Canada. The results revealed that the updraft speed reached up to 12 m/s. The entrainment mixed the mid and upper-level dry air and lowered the atmospheric moisture. The mid-level and upper-level dew point temperature changed by 5–10 ∘ C in a short period of time. The buoyant air strengthened the ascent as soon as the nocturnal inversion was eliminated by daytime heating. The 887 J/kg total increase of CAPE in less than 5 h and the high bulk Richardson number (BRN) of 93 were indicators of the growing pyro-cumulus cell. The presented simulation has not improved the original model or supported leading-edge numerical weather prediction (NWP) achievements, except for adapting WRF-Fire for Canadian biomass fuel. However, we were able to present a great deal of improvements in wildfire nowcasting and short-term forecasting to save lives and costs associated with wildfires. The simulation is sufficiently fast and efficient to be considered for a real-time operational model. While the project was designed and succeeded as an NWP application, we are still searching for a solution for the intractable problems associated with political borders and the current liable authorities for the further development of a new generation of national atmosphere–wildfire forecasting systems.

Influence of the geostrophic wind direction on the atmospheric boundary layer flow

Proper simulation and modelling of geophysical flows is crucial to the study of numerical weather prediction, wind energy and many other applications. When simulating the atmospheric boundary layer, Coriolis forces act as a result of Earth’s rotation. The horizontal component of Earth’s rotation, which is often neglected, influences the balance of vertical momentum. The horizontal component results in systematic differences in the structure and statistics of stratified atmospheric boundary layers as a function of the direction of the geostrophic velocity. These differences are particularly relevant to atmospheric flows which include inhomogeneous roughness elements such as drag disks or wind turbines since the presence of these drag elements alters the balance between turbulent stresses and the Coriolis contributions in Reynolds stress budgets. Even at latitudes as high as , changing the geostrophic wind velocity vector direction alone changes the magnitude of shear stress, and therefore vertical transport of kinetic energy, in the conventionally neutral atmospheric boundary layer up to . As such, the boundary layer height, shear and veer profiles, surface friction velocity and other key features are affected by the direction of the geostrophic wind. The influence of the horizontal component of Earth’s rotation in stable nocturnal boundary layers depends on the strength of the stratification as there is a strong influence in the present study and a weak influence in the GEWEX Atmospheric Boundary Layer Study (GABLS) case. A model of the effect of the horizontal component on the boundary layer shear stress is also proposed and verified with the present simulations. While not studied here, the present observations are also relevant to the oceanic Ekman boundary layer.

Parametrizing Horizontally-Averaged Wind and Temperature Profiles in the Urban Roughness Sublayer

Tower-based measurements from within and above the urban canopy in two cities are used to evaluate several existing approaches that parametrize the vertical profiles of wind speed and temperature within the urban roughness sublayer (RSL). It is shown that current use of Monin–Obukhov similarity theory (MOST) in numerical weather prediction models can be improved upon by using RSL corrections when modelling the vertical profiles of wind speed and friction velocity in the urban RSL using MOST. Using anisotropic building morphological information improves the agreement between observed and parametrized profiles of wind speed and momentum fluxes for selected methods. The largest improvement is found when using dynamically-varying aerodynamic roughness length and displacement height. Adding a RSL correction to MOST, however, does not improve the parametrization of the vertical profiles of temperature and heat fluxes. This is expected since sources and sinks of heat are assumed uniformly distributed through a simple flux boundary condition in all RSL formulations, yet are highly patchy and anisotropic in a real urban context. Our results can be used to inform the choice of surface-layer representations for air quality, dispersion, and numerical weather prediction applications in the urban environment.

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.

Processing and quality control of FY-3C GNOS data used in numerical weather prediction applications

Abstract. The Chinese radio occultation sounder GNOS (Global Navigation Occultation Sounder) is on the FY-3C satellite, which was launched on 23 September 2013. Currently, GNOS data are transmitted via the Global Telecommunications System (GTS), providing 450–500 profiles per day for numerical weather prediction applications. This paper describes the processing of the GNOS profiles with large biases related to L2 signal degradation. A new extrapolation procedure in bending angle space corrects the L2 bending angles using a thin ionosphere model and the fitting relationship between L1 and L2. We apply the approach to improve the L2 extrapolation of GNOS. The new method can effectively eliminate about 90 % of large departures. In addition to the procedure for the L2 degradation, this paper also describes our quality control (QC) for FY-3C GNOS. A noise estimate for the new L2 extrapolation can be used as a QC parameter to evaluate the performance of the extrapolation. A statistical comparison between GNOS bending angles and short-range ECMWF (European Centre for Medium-Range Weather Forecasts) forecast bending angles demonstrates that GNOS performs almost as well as the Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS), especially in the core region from around 10 to 35 km. The GNOS data with the new L2 extrapolation are suitable for assimilation into numerical weather prediction systems.

Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.

Towards an adjoint based 4D-Var state estimation for turbulent flow

The turbulent nature of the atmospheric boundary layer leads to wind-turbine power fluctuations and fatigue loading. The possibility to perform faster than real-time turbulent flow field simulations opens up applications in the forecasting and potentially actively controlling of turbulent structures. An important aspect of the forecasting, is the initialization of the simulation based on measurements, which will be the focus of this work. To this end, we use a 4D-Var approach, which is already being applied in numerical weather prediction applications. In this article, a fully developed pressure-driven atmospheric boundary layer is used as a case study. As a underlying model for the state estimation, we use a large eddy simulation (LES) code run on a relatively coarse grid, to decrease the model evaluation time. We take virtual measurements from a reference LES simulation, which is also used to benchmark the state estimation. For the 4D-Var optimization problem, we use a L-BFGS approach combined with an adjoint LES simulation for the gradient calculations.

Strong scaling for numerical weather prediction at petascale with the atmospheric model NUMA

Numerical weather prediction (NWP) has proven to be computationally challenging due to its inherent multiscale nature. Currently, the highest resolution global NWP models use a horizontal resolution of 9 km. At this resolution, many important processes in the atmosphere are not resolved. Needless to say, this introduces errors. In order to increase the resolution of NWP models, highly scalable atmospheric models are needed. The non-hydrostatic unified model of the atmosphere (NUMA), developed by the authors at the Naval Postgraduate School, was designed to achieve this purpose. NUMA is used by the Naval Research Laboratory, Monterey as the engine inside its next generation weather prediction system NEPTUNE. NUMA solves the fully compressible Navier–Stokes equations by means of high-order Galerkin methods (both spectral element as well as discontinuous Galerkin methods can be used). NUMA is capable of running middle and upper atmosphere simulations since it does not make use of the shallow-atmosphere approximation. This article presents the performance analysis and optimization of the spectral element version of NUMA. The performance at different optimization stages is analyzed using a theoretical performance model as well as measurements via hardware counters. Machine-independent optimization is compared to machine-specific optimization using Blue Gene (BG)/Q vector intrinsics. The best portable version of the main computations was found to be about two times slower than the best non-portable version. By using vector intrinsics, the main computations reach 1.2 PFlops on the entire IBM Blue Gene supercomputer Mira (12% of the theoretical peak performance). The article also presents scalability studies for two idealized test cases that are relevant for NWP applications. The atmospheric model NUMA delivers an excellent strong scaling efficiency of 99% on the entire supercomputer Mira using a mesh with 1.8 billion grid points. This allows running a global forecast of a baroclinic wave test case at a 3-km uniform horizontal resolution and double precision within the time frame required for operational weather prediction.

Space-borne profiling of atmospheric thermodynamic variables with Raman lidar: performance simulations.

The performance of a space-borne water vapour and temperature lidar exploiting the vibrational and pure rotational Raman techniques in the ultraviolet is simulated. This paper discusses simulations under a variety of environmental and climate scenarios. Simulations demonstrate the capability of Raman lidars deployed on-board low-Earth-orbit satellites to provide global-scale water vapour mixing ratio and temperature measurements in the lower to middle troposphere, with accuracies exceeding most observational requirements for numerical weather prediction (NWP) and climate research applications. These performances are especially attractive for measurements in the low troposphere in order to close the most critical gaps in the current earth observation system. In all climate zones, considering vertical and horizontal resolutions of 200 m and 50 km, respectively, mean water vapour mixing ratio profiling precision from the surface up to an altitude of 4 km is simulated to be 10%, while temperature profiling precision is simulated to be 0.40-0.75 K in the altitude interval up to 15 km. Performances in the presence of clouds are also simulated. Measurements are found to be possible above and below cirrus clouds with an optical thickness of 0.3. This combination of accuracy and vertical resolution cannot be achieved with any other space borne remote sensing technique and will provide a breakthrough in our knowledge of global and regional water and energy cycles, as well as in the quality of short- to medium-range weather forecasts. Besides providing a comprehensive set of simulations, this paper also provides an insight into specific possible technological solutions that are proposed for the implementation of a space-borne Raman lidar system. These solutions refer to technological breakthroughs gained during the last decade in the design and development of specific lidar devices and sub-systems, primarily in high-power, high-efficiency solid-state laser sources, low-weight large aperture telescopes, and high-gain, high-quantum efficiency detectors.

A simulated observation database to assess the impact of the IASI-NG hyperspectral infrared sounder

Abstract. The highly accurate measurements of the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) are used in numerical weather prediction (NWP), atmospheric chemistry and climate monitoring. As the second generation of the European Polar System (EPS-SG) is being developed, a new generation of IASI instruments has been designed to fly on board the MetOp-SG constellation: IASI New Generation (IASI-NG). In order to prepare the arrival of this new instrument, and to evaluate its impact on NWP and atmospheric chemistry applications, a set of IASI and IASI-NG simulated data was built and made available to the public to set a common framework for future impact studies. This paper describes the information available in this database and the procedure followed to run the IASI and IASI-NG simulations. These simulated data were evaluated by comparing IASI-NG to IASI observations. The result is also presented here. Additionally, preliminary impact studies of the benefit of IASI-NG compared to IASI on the retrieval of temperature and humidity in a NWP framework are also shown in the present work. With a channel dataset located in the same wave numbers for both instruments, we showed an improvement of the temperature retrievals throughout the atmosphere, with a maximum in the troposphere with IASI-NG and a lower benefit for the tropospheric humidity.

Atlas : A library for numerical weather prediction and climate modelling

The algorithms underlying numerical weather prediction (NWP) and climate models that have been developed in the past few decades face an increasing challenge caused by the paradigm shift imposed by hardware vendors towards more energy-efficient devices. In order to provide a sustainable path to exascale High Performance Computing (HPC), applications become increasingly restricted by energy consumption. As a result, the emerging diverse and complex hardware solutions have a large impact on the programming models traditionally used in NWP software, triggering a rethink of design choices for future massively parallel software frameworks. In this paper, we present Atlas, a new software library that is currently being developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), with the scope of handling data structures required for NWP applications in a flexible and massively parallel way. Atlas provides a versatile framework for the future development of efficient NWP and climate applications on emerging HPC architectures. The applications range from full Earth system models, to specific tools required for post-processing weather forecast products. The Atlas library thus constitutes a step towards affordable exascale high-performance simulations by providing the necessary abstractions that facilitate the application in heterogeneous HPC environments by promoting the co-design of NWP algorithms with the underlying hardware.

Bias characterization of CrIS radiances at 399 selected channels with respect to NWP model simulations

The Cross-track Infrared Sounder (CrIS) on board the Suomi National Polar-Orbiting Partnership (S-NPP) satellite is a hyperspectral Fourier Transform Spectrometer. In this study, biases of the 399 channels used in numerical weather prediction applications are characterized based on the differences between CrIS observations and model simulations in clear-sky conditions over ocean. The Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask is used for selecting CrIS clear-sky data. The global mean biases are within ±1K for most channels (more than 390). Biases for the channels not affected by trace gases other than water vapor in long-wave infrared (LWIR), middle-wave infrared (MWIR) and short-wave infrared (SWIR) bands vary from about −0.5 to 0.3K, −0.2 to 0.8K, and −0.1 to 0.9K, respectively. The scan-angle variations of biases are less than ±0.4K for all channels. The MWIR sounding channels have larger biases in middle and high latitudes than the tropics, which might be associated with latitudinal differences of water vapor variability. The SWIR CO2 channels are affected by nonlocal thermodynamic equilibrium (NLTE) in the upper stratosphere and the window channels could be affected by the sun glint effect, both of which are considered in model simulations. Biases of the NLTE affected channels during daytime are found to be 0.5–1K higher than nighttime. The SWIR window channels have colder biases at Field of Regards (FORs) 6–15 than the other scan positions due to the sun glint effect.

Quality and Impact of Indian Doppler Weather Radar Wind Profiles: A Diagnostic Study

In the tropics, efficient weather forecasts require high-quality vertical profiles of winds to overcome improper coupling of mass and wind fields and balance relationships in the region. The India Meteorological Department (IMD) operates the network of Doppler Weather Radar (DWR) in microwave frequencies (S-band or C-band) at various locations in India. The National Centre for Medium Range Weather Forecasting (NCMRWF) receives the volume velocity processing (VVP) wind profiles from all DWRs through the Global Telecommunication System (GTS) network in near real time. The radar VVP wind is a mean horizontal wind derived at different heights from radial velocities suitable for numerical weather prediction applications. Three numerical experiments, CNTL (without VVP winds), 3DVAR and HYBRID with the assimilation of VVP winds by means of 3-dimensional variational (3dvar) and hybrid data assimilation systems were conducted using the NCMRWF Global Forecast System (NGFS) model. This study had two objectives: (1) quality assessment of VVP winds and (2) investigation of the impact of VVP wind profiles on NGFS model forecast. The quality of VVP wind profiles was assessed against the NGFS model background and radiosonde wind profiles. The absolute values of zonal and meridional wind observation minus background (O−B) increased with the pressure for all DWRs. All radars exhibited the accepted (rejected) ratio as a decreasing (increasing) function of pressure. The resemblance between the zonal and meridional O−B statistics for 3DVAR and HYBRID experiments is apparently remarkable. The accepted VVP winds and radiosonde winds in both experiments (3DVAR and HYBRID) were consistent. The correlation coefficient (R) was higher at Patna (Patiala) for zonal (meridional) winds in the 3DVAR experiment and at Patna (Jaipur) in the HYBRID experiment. At Chennai, the R value was lower in both the experiments for both wind components. However, because of the assimilation of VVP winds by means of 3dvar, the root-mean-square error (RMSE) of zonal and meridional winds improved by approximately 2–3% up to the day 5 forecast in the analysis performed below 700 hPa. Further improvement in RMSE by approximately 5% was observed in both the wind components because of the hybrid data assimilation. The zonal and meridional wind RMSEs in the HYBRID (3DVAR) experiment improved by ≈5 (3)% compared with the 3DVAR (CNTL) experiment.