Numerical Weather Prediction Community Research Articles

Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands

Abstract. Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

Experiments on the flow over a hill covered by a canopy in stably stratified conditions

It has long been suspected that thermo-topographic flows, especially gravity currents, within vegetation canopies on complex terrain are one of the main reasons behind the failure to reconcile micrometeorological and biometric estimates of canopy-atmosphere exchange at many sites. However, the physical mechanisms governing the initiation and the scaling of these flows remain poorly understood. Here we present the results of a novel wind tunnel study that looks in detail at the flow within and above an open canopy in stably stratified conditions and investigates the physical mechanisms responsible for gravity currents within canopies. The wind tunnel simulations demonstrate that gravity currents are established through a complex balance of competing forces on the flow within the canopy. Three forcing terms act on the flow in the canopy as it passes over the hill. First is the hydrodynamic pressure gradient associated with the boundary layer flow aloft; second, a hydrostatic pressure gradient associated with the displacement of temperature and density surfaces by the hill, and finally a thermal wind term, where a streamwise pressure gradient is caused by changes in the depth of the temperature perturbations to the flow. The net balance of these forces is opposed by the canopy drag. Gravity currents, however, do not appear unless the turbulence, which supports the transport of momentum into the canopy, is also reduced. This suppression occurs preferentially deep within the canopy due to a Richardson number cut-off effect, which is directly linked to the different transport mechanisms of heat and momentum across the boundary layers on the canopy elements. The gravity current first appears at the ground surface, despite cooling profiles that are concentrated in the upper canopy. Once initiated, a gravity current can propagate substantial distances away from the triggering topography, driven by the thermal wind term. If shown to be robust these results have widespread implications for the micrometeorology, atmospheric boundary layer and numerical weather prediction communities.

The Uncoordinated Giant II: Why U.S. Operational Numerical Weather Prediction Is Still Lagging and How to Fix It

Abstract U.S. numerical weather prediction (NWP) is critical for both economic reasons and the protection of life and property. Unfortunately, the nation’s global model prediction skill substantially lags the performance of leading international NWP centers, with problems extending to regional prediction. This paper reviews the history of U.S. activities in NWP, describes why U.S. weather prediction is not fulfilling its potential, and proposes actions that could restore U.S NWP to world leadership. Key suggestions include the creation of a U.S. Numerical Weather Prediction Center, better coordination of the efforts of the research and operational NWP communities, and increasing NOAA computational resources by at least tenfold.

Skill of Medium-Range Forecast Models Using the Same Initial Conditions

Abstract In the Different Models, Same Initial Conditions (DIMOSIC) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are 1) to evaluate the forecast skill and how it depends on model formulation, 2) to assess systematic differences and errors at short lead times, 3) to compare multimodel ensemble spread to model uncertainty schemes, and 4) to identify models that generate similar solutions. Our results show that all models in this study are capable of producing high-quality forecasts given a high-quality analysis. But at the same time, we find a large variety in model biases, both in terms of temperature errors and precipitation. We are able to identify models whose forecasts are more similar to each other than they are to those of other systems, due to the use of similar model physics packages. However, in terms of multimodel ensemble spread, our results also demonstrate that forecast sensitivities to different model formulations still are substantial. We therefore believe that the diversity in model design that stems from parallel development efforts at global modeling centers around the world remains valuable for future progress in the numerical weather prediction community.

Radar Reflectivity–Based Model Initialization Using Specified Latent Heating (Radar-LHI) within a Diabatic Digital Filter or Pre-Forecast Integration

Abstract A technique for model initialization using three-dimensional radar reflectivity data has been developed and applied within the NOAA 13-km Rapid Refresh (RAP) and 3-km High-Resolution Rapid Refresh (HRRR) regional forecast systems. This technique enabled the first assimilation of radar reflectivity data for operational NOAA forecast models, critical especially for more accurate short-range prediction of convective storms. For the RAP, the technique uses a diabatic digital filter initialization (DFI) procedure originally deployed to control initial inertial gravity wave noise. Within the forward-model integration portion of diabatic DFI, temperature tendencies obtained from the model cloud/precipitation processes are replaced by specified latent heating–based temperature tendencies derived from the three-dimensional radar reflectivity data, where available. To further refine initial conditions for the convection-allowing HRRR model, a similar procedure is used in the HRRR, but without DFI. Both of these procedures, together called the “Radar-LHI” (latent heating initialization) technique, have been essential for initialization of ongoing precipitation systems, especially convective systems, within all NOAA operational versions of the 13-km RAP and 3-km HRRR models extending through the latest implementation upgrade at NCEP in 2020. Application of the latent heat–derived temperature tendency induces a vertical circulation with low-level convergence and upper-level divergence in precipitation systems. Retrospective tests of the Radar-LHI technique show significant improvement in short-range (0–6 h) precipitation system forecasts, as revealed by reflectivity verification scores. Results presented document the impact on HRRR reflectivity forecasts of the radar reflectivity initialization technique applied to the RAP alone, HRRR alone, and both the RAP and HRRR. Significance Statement The large forecast uncertainty of convective situations, even at short lead times, coupled with the hazardous weather they produce, makes convective storm prediction one of the most significant short-range forecast challenges confronting the operational numerical weather prediction community. Prediction of heavy precipitation events also requires accurate initialization of precipitation systems. An innovative assimilation technique using radar reflectivity data to initialize NOAA operational weather prediction models is described. This technique, which uses latent heating specified from radar reflectivity (and can accommodate lightning data and other convection/precipitation indicators), was first implemented in 2009 at NOAA/NCEP and continues to be used in 2022 in the NCEP-operational RAP and HRRR models, making it a backbone of the NOAA rapidly updated numerical weather prediction capability.

FY-3E: The First Operational Meteorological Satellite Mission in an Early Morning Orbit

Fengyun-3E (FY-3E), the world’s first early-morning-orbit meteorological satellite for civil use, was launched successfully at the Jiuquan Satellite Launch Center on 5 July 2021. The FY-3E satellite will fill the vacancy of the global early-morning-orbit satellite observation, working together with the FY-3C and FY-3D satellites to achieve the data coverage of early morning, morning, and afternoon orbits. The combination of these three satellites will provide global data coverage for numerical weather prediction (NWP) at 6-hour intervals, effectively improving the accuracy and time efficiency of global NWP, which is of great significance to perfect the global earth observing system. In this article, the background and meteorological requirements for the early-morning-orbit satellite are reviewed, and the specifications of the FY-3E satellite, as well as the characteristics of the onboard instrumentation for earth observations, are also introduced. In addition, the ground segment and the retrieved geophysical products are also presented. It is believed that the NWP communities will significantly benefit from an optimal temporal distribution of observations provided by the early morning, mid-morning, and afternoon satellite missions. Further benefits are expected in numerous applications such as the monitoring of severe weather/climate events, the development of improved sampling designs of the diurnal cycle for accurate climate data records, more efficient monitoring of air quality by thermal infrared remote sensing, and the quasi-continuous monitoring of the sun for space weather and climate.

Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database

We study the applicability of tools developed by the computer vision community for features learning and semantic image inpainting to perform data reconstruction of fluid turbulence configurations. The aim is twofold. First, we explore on a quantitative basis, the capability of Convolutional Neural Networks embedded in a Deep Generative Adversarial Model (Deep-GAN) to generate missing data in turbulence, a paradigmatic high dimensional chaotic system. In particular, we investigate their use in reconstructing two-dimensional damaged snapshots extracted from a large database of numerical configurations of 3d turbulence in the presence of rotation, a case with multi-scale random features where both large-scale organised structures and small-scale highly intermittent and non-Gaussian fluctuations are present. Second, following a reverse engineering approach, we aim to rank the input flow properties (features) in terms of their qualitative and quantitative importance to obtain a better set of reconstructed fields. We present two approaches both based on Context Encoders. The first one infers the missing data via a minimization of the L2 pixel-wise reconstruction loss, plus a small adversarial penalisation. The second searches for the closest encoding of the corrupted flow configuration from a previously trained generator. Finally, we present a comparison with a different data assimilation tool, based on Nudging, an equation-informed unbiased protocol, well known in the numerical weather prediction community. The TURB-Rot database, http://smart-turb.roma2.infn.it, of roughly 300K 2d turbulent images is released and details on how to download it are given.

Development of an Operational Hybrid Data Assimilation System at KIAPS

This study introduces the operational data assimilation (DA) system at the Korea Institute of Atmospheric Prediction Systems (KIAPS) to the numerical weather prediction community. Its development history and performance are addressed with experimental illustrations and the authors’ previously published studies. Milestones in skill improvements include the initial operational implementation of three-dimensional variational data assimilation (3DVar), the ingestion of additional satellite observations, and changing the DA scheme to a hybrid four-dimensional ensemble-variational DA using forecasts from an ensemble based on the local ensemble transform Kalman filter (LETKF). In the hybrid system, determining the relative contribution of the ensemble-based covariance to the resultant analysis is crucial, particularly for moisture variables including a variety of horizontal scale spectra. Modifications to the humidity control variable, partial rather than full recentering of the ensemble for humidity further improves moisture analysis, and the inclusion of more radiance observations with higher-level peaking channels have significant impacts on stratosphere temperature and wind performance. Recent update of the operational hybrid DA system relative to the previous 3DVar system is described for detailed improvements with interpretation.

The primacy of doubt: Evolution of numerical weather prediction from determinism to probability

AbstractOver the last 25 years, the focus of operational numerical weather prediction has evolved from that of estimating the most likely evolution of weather to that of estimating probability distributions of future weather associated with inevitable uncertainties in both initial conditions and model equations. This evolution from determinism to uncertainty has not only increased the scientific rigor of weather prediction, it has also increased the value of weather forecasts for users. In addition, it has opened up a new approach to solving the equations of motion, likely to be of importance for both weather and climate prediction in an age where high‐performance computing is limited by power consumption. However, despite all this, the numerical weather prediction community has yet to embrace fully the concept of the primacy of doubt. It is now time to take the final step in this direction.

RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

Abstract. Ground-based microwave radiometers (MWRs) offer a new capability to provide continuous observations of the atmospheric thermodynamic state in the planetary boundary layer. Thus, they are potential candidates to supplement radiosonde network and satellite data to improve numerical weather prediction (NWP) models through a variational assimilation of their data. However in order to assimilate MWR observations, a fast radiative transfer model is required and such a model is not currently available. This is necessary for going from the model state vector space to the observation space at every observation point. The fast radiative transfer model RTTOV is well accepted in the NWP community, though it was developed to simulate satellite observations only. In this work, the RTTOV code has been modified to allow for simulations of ground-based upward-looking microwave sensors. In addition, the tangent linear, adjoint, and K-modules of RTTOV have been adapted to provide Jacobians (i.e., the sensitivity of observations to the atmospheric thermodynamical state) for ground-based geometry. These modules are necessary for the fast minimization of the cost function in a variational assimilation scheme. The proposed ground-based version of RTTOV, called RTTOV-gb, has been validated against accurate and less time-efficient line-by-line radiative transfer models. In the frequency range commonly used for temperature and humidity profiling (22–60 GHz), root-mean-square brightness temperature differences are smaller than typical MWR uncertainties (∼ 0.5 K) at all channels used in this analysis. Brightness temperatures (TBs) computed with RTTOV-gb from radiosonde profiles have been compared with nearly simultaneous and co-located ground-based MWR observations. Differences between simulated and measured TBs are below 0.5 K for all channels except for the water vapor band, where most of the uncertainty comes from instrumental errors. The Jacobians calculated with the K-module of RTTOV-gb have been compared with those calculated with the brute force technique and those from the line-by-line model ARTS. Jacobians are found to be almost identical, except for liquid water content Jacobians for which a 10 % difference between ARTS and RTTOV-gb at transparent channels around 450 hPa is attributed to differences in liquid water absorption models. Finally, RTTOV-gb has been applied as the forward model operator within a one-dimensional variational (1D-Var) software tool in an Observing System Simulation Experiment (OSSE). For both temperature and humidity profiles, the 1D-Var with RTTOV-gb improves the retrievals with respect to the NWP model in the first few kilometers from the ground.

Enhancing finite differences with radial basis functions: Experiments on the Navier–Stokes equations

Polynomials are used together with polyharmonic spline (PHS) radial basis functions (RBFs) to create local RBF-finite-difference (RBF-FD) weights on different node layouts for spatial discretizations that can be viewed as enhancements of the classical finite differences (FD). The presented method replicates the convergence properties of FD but for arbitrary node layouts. It is tested on the 2D compressible Navier–Stokes equations at low Mach number, relevant to atmospheric flows. Test cases are taken from the numerical weather prediction community and solved on bounded domains. Thus, attention is given on how to handle boundaries with the RBF-FD method, as well as a novel implementation for hyperviscosity. Comparisons are done on Cartesian, hexagonal, and quasi-uniform node layouts. Consideration and guidelines are given on PHS order, polynomial degree and stencil size. The main advantages of the present method are: 1) capturing the basic physics of the problem surprisingly well, even at very coarse resolutions, 2) high-order accuracy without the need of tuning a shape parameter, and 3) the inclusion of polynomials eliminates stagnation (saturation) errors. A MATLAB code is given to calculate the differentiation weights for this novel approach.

Oceansat-II Scatterometer: Sensor Performance Evaluation, <inline-formula> <tex-math notation="TeX">$\sigma^{0}$</tex-math></inline-formula> Analyses, and Estimation of Biases

The Oceansat-II Scatterometer has completed two years in orbit. The instrument has been declared operational, and the normalized radar cross section (σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> ) and wind products are being made routinely available to the global operational Numerical Weather Prediction community. The σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> data from the sensor have been rigorously analyzed for the past two years. Efforts have been put to systematically correlate the biases observed in the data to the onboard functionality of the instrument and to precisely quantify these biases. These analyses have helped not only in the refinement of the ground-processing algorithm but also in the evaluation of sensor performance. This paper presents some of the analyses that have been carried out related to instrument noise calibration with reference to deep-space observations, estimation of biases in the signal bandwidth, and estimation of fixed remnant attitude biases. This paper also addresses the means for rectifying these instrument-related biases.

Importance of temporal symmetry in spatial discretization for geostrophic adjustment in semi‐implicit Z‐grid schemes

Among the dynamical cores of numerical weather prediction communities, many different discretization methods can be distinguished to solve the equations governing the motions in the atmosphere numerically. One of them, the Z‐grid approach, is based on solving the equations formulated in terms of divergence and vorticity on an Arakawa A‐grid, a grid where all the variables are defined at the same grid points. To permit an efficient semi‐implicit (SI) treatment, Z‐grid schemes were proposed in the literature that first perform SI time discretization on the momentum equations formulated in terms of velocity components in order to construct from this a discretized divergence equation. This publication shows that a careful formulation of such SI Z‐grid schemes is required to conserve appropriate dispersion relations for inertia–gravity, inertia–Lamb and Rossby waves. It is proven analytically for a two time‐level (2TL) SI Z‐grid scheme of the 1D shallow‐water equations that the spatial discretization must respect temporal symmetry, meaning that the spatial discretization must be identical in the implicit and explicit parts of the scheme. If not, the discretized waves are damped or amplified and their phase and group velocity may be seriously distorted. These findings are discussed in detail and both 1D and 2D numerical tests are carried out to demonstrate that a symmetric formulation is an important modelling constraint in order to obtain an appropriate geostrophic adjustment.

Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges

Traditionally, the nowcasting of precipitation was conducted to a large extent by means of extrapolation of observations, especially of radar ref lectivity. In recent years, the blending of traditional extrapolation-based techniques with high-resolution numerical weather prediction (NWP) is gaining popularity in the nowcasting community. The increased need of NWP products in nowcasting applications poses great challenges to the NWP community because the nowcasting application of high-resolution NWP has higher requirements on the quality and content of the initial conditions compared to longer-range NWP. Considerable progress has been made in the use of NWP for nowcasting thanks to the increase in computational resources, advancement of high-resolution data assimilation techniques, and improvement of convective-permitting numerical modeling. This paper summarizes the recent progress and discusses some of the challenges for future advancement.

Rotational and divergent kinetic energy in the mesoscale model ALADIN

Kinetic energy spectra from the mesoscale numerical weather prediction (NWP) model ALADIN with horizontal resolution 4.4 km are split into divergent and rotational components which are then compared at horizontal scales below 300 km and various vertical levels. It is shown that about 50% of kinetic energy in the free troposphere in ALADIN is divergent energy. The percentage increases towards 70% near the surface and in the upper troposphere towards 100 hPa. The maximal percentage of divergent energy is found at stratospheric levels around 100 hPa and at scales below 100 km which are not represented by the global models. At all levels, the divergent energy spectra are characterised by shallower slopes than the rotational energy spectra, and the difference increases as horizontal scales become larger. A very similar vertical distribution of divergent energy is obtained by using the standard ALADIN approach for the computation of spectra based on the extension zone and by applying detrending approach commonly used in mesoscale NWP community.

GOES-R Advanced Baseline Imager: spectral response functions and radiometric biases with the NPP Visible Infrared Imaging Radiometer Suite evaluated for desert calibration sites

The Advanced Baseline Imager (ABI), which will be launched in late 2015 on the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellite R-series satellite, will be evaluated in terms of its data quality postlaunch through comparisons with other satellite sensors such as the recently launched Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite. The ABI has completed much of its prelaunch characterization and its developers have generated and released its channel spectral response functions (response versus wavelength). Using these responses and constraining a radiative transfer model with ground reflectance, aerosol, and water vapor measurements, we simulate observed top of atmosphere (TOA) reflectances for analogous visible and near infrared channels of the VIIRS and ABI sensors at the Sonoran Desert and White Sands National Monument sites and calculate the radiometric biases and their uncertainties. We also calculate sensor TOA reflectances using aircraft hyperspectral data from the Airborne Visible/Infrared Imaging Spectrometer to validate the uncertainties in several of the ABI and VIIRS channels and discuss the potential for validating the others. Once on-orbit, calibration scientists can use these biases to ensure ABI data quality and consistency to support the numerical weather prediction community and other data users. They can also use the results for ABI or VIIRS anomaly detection and resolution.

Assimilating AIRS Temperature and Mixing Ratio Profiles Using an Ensemble Kalman Filter Approach for Convective-Scale Forecasts

Abstract One satellite data product that has received great interest in the numerical weather prediction community is the temperature and mixing ratio profiles derived from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite. This research assesses the impact of assimilating AIRS profiles on high-resolution ensemble forecasts of southern plains severe weather events occurring on 26 May 2009 and 10 May 2010 by comparing two ensemble forecasts. In one ensemble, the 1830 and 2000 UTC level 2 AIRS temperature and dewpoint profiles are assimilated with all other routine observations into a 36-member, 15-km Weather and Research Forecast Model (WRF) ensemble using a Kalman filter approach. The other ensemble is identical, except that only routine observations are assimilated. In addition, 3-km one-way nested-grid ensemble forecasts are produced during the periods of convection. Results indicate that over the contiguous United States, the AIRS profiles do not measurably improve the ensemble mean forecasts of midtropospheric temperature and dewpoint. However, the ensemble mean dewpoint profiles in the region of severe convective development are improved by the AIRS assimilation. Comparisons of the forecast ensemble radar reflectivity probabilities between the 1- and 4-h forecast times with nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) observations show that AIRS-enhanced ensembles consistently generate more skillful forecasts of the convective features at these times.

Atmospheric radiative transfer modeling: a summary of the AER codes

The radiative transfer models developed at AER are being used extensively for a wide range of applications in the atmospheric sciences. This communication is intended to provide a coherent summary of the various radiative transfer models and associated databases publicly available from AER ( http://www.rtweb.aer.com). Among the communities using the models are the remote sensing community (e.g. TES, IASI), the numerical weather prediction community (e.g. ECMWF, NCEP GFS, WRF, MM5), and the climate community (e.g. ECHAM5). Included in this communication is a description of the central features and recent updates for the following models: the line-by-line radiative transfer model (LBLRTM); the line file creation program (LNFL); the longwave and shortwave rapid radiative transfer models, RRTM_LW and RRTM_SW; the Monochromatic Radiative Transfer Model (MonoRTM); the MT_CKD Continuum; and the Kurucz Solar Source Function. LBLRTM and the associated line parameter database (e.g. HITRAN 2000 with 2001 updates) play a central role in the suite of models. The physics adopted for LBLRTM has been extensively analyzed in the context of closure experiments involving the evaluation of the model inputs (e.g. atmospheric state), spectral radiative measurements and the spectral model output. The rapid radiative transfer models are then developed and evaluated using the validated LBLRTM model.

Combined estimates improve high‐resolution coastal wind mapping

The operational meteorological community and the numerical weather prediction community share a common need for high‐resolution estimates of the near‐surface wind field in data‐sparse regions of the globe, such as the coastal zones of Alaska and the Pacific Northwest. Wind estimates over coastal waters using conventional multiple‐antenna scatterometers or passive microwave sensors are difficult to obtain because the large footprint associated with these sensors results in significant contamination from land. Since synthetic aperture radar (SAR) can provide high‐resolution imagery of the surface virtually up to the shoreline, “SAR scatterometry” represents a potentially significant new technique for measuring ocean‐surface wind fields at resolutions more than an order of magnitude finer than is now possible with any other spaceborne technique.

Diagnosis and Correction of Systematic Humidity Error in a Global Numerical Weather Prediction Model

Abstract Accuracy of humidity forecasts has been considered relatively unimportant to much of the operational numerical weather prediction (NWP) community. However, the U.S. Air Force is interested in accurate water vapor and cloud forecasts as end products. It is expected that the NWP community as a whole will become more involved in improving their humidity forecasts as they recognize the important role of accurate water vapor distributions in data assimilation, forecasts of temperature and precipitation, and climate change research. As a modeling community, we need to begin now to identify and rectify the systematic humidity forecast errors that are present in NWP models. This will allow us to take full advantage of the new types of remotely sensed water vapor and cloud measurements that are on the horizon. The research reported in this paper attempts to address this issue in a simple, straightforward manner, using the Phillips Laboratory Global Spectral Model (PL GSM). It was found that significant sy...