Operational NWP Research Articles

Studying infrasound propagation in the middle atmosphere with ICON and UA-ICON: comparison with the IFS and ground-based remote sensing

Infrasound signals are used to monitor various anthropogenic and natural sources. In particular, infrasound is one of the four verification technologies used by the International Monitoring System (IMS) of the Comprehensive Test-Ban Treaty organisation (CTBTO). To determine accurate source locations and energies, an accurate model of wind and temperature up to the lower thermosphere is necessary, hence operational NWP products are of great importance for routine infrasound monitoring activities. However, many of these models focus on tropospheric conditions, and the middle atmosphere (MA) is not well represented. UA-ICON is an upper atmosphere version of the ICON model that provides modelled atmospheric parameters up to 150 km. First, to assess ICON and IFS operational analysis products, comparisons to lidar observations are made. The main differences between both products were analysed with respect to winds and temperatures in the MA. Second, UA-ICON simulations outputs were compared to ICON and IFS fields to demonstrate the increased wave activity above ~30 km with UA-ICON. The added value of UA-ICON with respect to ICON and IFS for infrasound propagation simulations is discussed. The comparisons between the instrumental observations and the models will be presented, as well as comparisons between modelled and measured infrasound propagation.

SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting

Data-driven medium-range weather forecasting has attracted much attention in recent years. However, the forecasting accuracy at high resolution is unsatisfactory currently. Pursuing high-resolution and high-quality weather forecasting, we develop a data-driven model SwinRDM which integrates an improved version of SwinRNN with a diffusion model. SwinRDM performs predictions at 0.25-degree resolution and achieves superior forecasting accuracy to IFS (Integrated Forecast System), the state-of-the-art operational NWP model, on representative atmospheric variables including 500 hPa geopotential (Z500), 850 hPa temperature (T850), 2-m temperature (T2M), and total precipitation (TP), at lead times of up to 5 days. We propose to leverage a two-step strategy to achieve high-resolution predictions at 0.25-degree considering the trade-off between computation memory and forecasting accuracy. Recurrent predictions for future atmospheric fields are firstly performed at 1.40625-degree resolution, and then a diffusion-based super-resolution model is leveraged to recover the high spatial resolution and finer-scale atmospheric details. SwinRDM pushes forward the performance and potential of data-driven models for a large margin towards operational applications.

Sensitivity of Nocturnal Warm Sector Rainfall Simulation to the Configuration of Initial and Lateral Boundary Conditions: A Case Study in Southern China Based on the Operational TRAMS Model

AbstractA heavy nocturnal warm sector rainfall occurred over the western coastal region of South China in June 2020, of which the precipitation magnitude was seriously underestimated by the operational TRAMS (Tropical Regional Atmosphere Model System) model. In this study, by improving the configuration of initial conditions (ICs) and lateral boundary conditions (LBCs) in offline nesting, the simulation of the convection initiation (CI) process can be well improved. The CI simulation is found to be sensitive to the vertical resolution in ICs in this case. When the low‐level vertical resolution of ICs is increased, the nocturnal near‐surface cold layer caused by inland mountains can be resolved better, which is necessary to form the convergence line along the coastline. Another important impact of increasing the vertical resolution of ICs is the successful simulation of horizontal convective rolls (HCRs) over the northern South China Sea. The HCRs apparently increase the depth of the warm‐moist marine boundary layer jet (MBLJ), and finally lead to the CI along the coastline. The increased vertical resolutions of LBCs can reduce the discontinuity of simulated moist tongue across the southern lateral boundary of the TRAMS model, which provides more moisture for the warm sector rainfall through MBLJ. LBCs with Higher temporal frequency help to alleviate the western position bias of rain belt by reducing the temporal interpolation error. Our study highlights the importance of finer ICs and LBCs in offline nesting for regional operational NWP systems in the South China region.

A Single-Column Method to Identify Sea and Land Breezes in Mesoscale-Resolving NWP Models

Abstract One of the most prominent mesoscale phenomena in the coastal zone is the sea-breeze/land-breeze circulation. The pattern and its implications for the weather in coastal areas are well described, and with mesoscale-resolving operational NWP models the circulation can be captured. In this study, a straightforward method to identify sea and land breezes based on the change in wind direction in the column above a grid point on the coastline is presented. The method was tested for southern Sweden using archived output from the HARMONIE-AROME model with promising results, describing both the seasonal and diurnal cycles well. In areas with a complex coastline, such as narrow straits, the concept of the land–sea breeze becomes less clear, and several ways to address this problem for the suggested method are discussed. With an operational index of the sea and land breezes, the forecaster can better understand and express the weather situation and add value for people in the coastal zone. Further, the indices can be used to study systematic biases in the model and to create climatologies of the sea and land breezes. Significance Statement A wind pattern that is frequently occurring in the coastal zone is the sea-breeze/land-breeze circulation, and the purpose of this study is to test a new method to automatically identify sea breezes and land breezes in weather forecasts. Knowing if a sea breeze or a land breeze is occurring is helpful for the operational weather forecaster in understanding the weather situation. It can also be used to study systematic model behavior, for example, errors in the forecast temperature during sea-breeze conditions. The method has been tested for seven coastal sites in Sweden and shows promising results both in case studies and multiyear statistics.

Effects of Distant Organized Convection on Forecasts of Widespread Clear-Air Turbulence

Abstract Two cases of observed widespread moderate-or-greater (MOG) clear-air turbulence (CAT) in different synoptic patterns are investigated using a nested high-resolution NWP model. Both of these cases occurred in confluent entrance regions of upper-tropospheric–lower-stratospheric (UTLS) jet streaks, where large-scale anticyclonic outflow from distant organized moist convection strengthened the UTLS jet. Both the strength and vertical sharpness of the resulting jet influence the altitudes of MOG turbulence and the details of simulated turbulence onset mechanisms. In a strong and narrow UTLS jet downstream of a weak synoptic ridge, MOG turbulence arises from Kelvin–Helmholtz (KH) waves that overturn in opposite directions on the vertical flanks of the jet. In broader UTLS jets, MOG turbulence arising from KH waves was favored in strong vertical shear layers beneath the wind maximum, but was inhibited above it due to static stability increases near the tropopause. However, vertically propagating internal gravity waves initiated above KH wave breaking beneath the UTLS jet amplify within the lower stratosphere above the jet, constituting another possible source of turbulence. Turbulence onset mechanisms were often apparent in simulations with minimum horizontal grid spacings of Δx = 1 km. However, amplitudes of the associated grid-resolved vertical motions were unreliable when compared with simulations having minimum horizontal grid spacings of Δx = 1/3 km. In spite of this, turbulence forecasting systems driven by input from coarser-resolution operational NWP models are demonstrated to provide good diagnoses of this type of convectively influenced CAT when the NWP model accurately forecasts upstream convection. Significance Statement In this study we document the role of distant convection on observed widespread strong clear-air turbulence near upper-level jet streams in different weather patterns. Results from nested NWP model simulations, together with similar examples from previous case studies, suggest a strong association of widespread turbulence with jet stream enhancements related to outflow from upstream convection. We also demonstrate how model horizontal grid spacings of <1 km are required to adequately resolve common turbulence onset mechanisms (e.g., Kelvin–Helmholtz instability, gravity wave breaking). Nevertheless, examples are provided showing that turbulence forecasting systems driven by lower-resolution operational NWP models can provide potentially valuable guidance on these widespread turbulence events when those models accurately forecast the timing and locations of upstream convection.

Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System

AbstractModel bias is one of the main obstacles to improved accuracy and reliability in numerical weather prediction conducted with state‐of‐the‐art atmospheric models. To deal with model bias, a modification of the standard four‐dimensional variational (4D‐Var) algorithm, called weak‐constraint 4D‐Var, has been developed where a forcing term is introduced into the model to correct for the bias that accumulates along the model trajectory. This approach reduced the temperature bias in the stratosphere by up to 50% and is implemented in the European Centre for Medium‐Range Weather Forecasts operational forecasting system. Despite different origins and applications, data assimilation (DA) and Deep Learning are both able to learn about the Earth system from observations. In this paper, a deep learning approach for model bias correction is developed using temperature retrievals from radio occultation (RO) measurements. Neural networks (NNs) require a large number of samples to properly capture the relationship between the temperature first‐guess trajectory and the model bias. As running the Integrated Forecasting System (IFS) DA system for extended periods of time with a fixed model version and at realistic resolutions is computationally very expensive, we have chosen to train, the initial NNs are trained using the ERA5 reanalysis before using transfer learning on 1 year of the current IFS model. Preliminary results show that convolutional NNs are adequate to estimate model bias from RO temperature retrievals. The different strengths and weaknesses of both deep learning and weak constraint 4D‐Var are discussed, highlighting the potential for each method to learn model biases effectively and adaptively.

APS2-ACCESS-C2: the first Australian operational NWP convection-permitting model

The Australian Bureau of Meteorology’s ‘Australian Parallel Suite’ (APS) operational numerical weather prediction regional Australian Community Climate and Earth-System Simulator (ACCESS) city-based system (APS1 ACCESS-C1) was updated in August 2017 with the commissioning of the APS2 ACCESS-C2. ACCESS-C2 runs over six regional domains. Significant upgrade changes included implementation of Unified Model 8.2 code; nesting in the 12 km resolution APS2 ACCESS-R2 regional model; and, importantly, an increased horizontal resolution from 4 to 1.5 km, enabling C2 to become the first Australian operational convection-permitting model (CPM). Traditional rainfall verification metrics and Fractions Skill Score show C2 forecast skill over ACCESS-C domains in summer and winter was generally, and in many cases, significantly better than C1. Case studies showed that C2 forecasts had better-detailed wind and precipitation fields, particularly at longer forecast ranges and higher rain rates. The improvements in C2 forecasts were principally due to its CPM ability to simulate high temporal and spatial resolution features, which continue to be of great interest to forecasters. C2 also laid the groundwork for the present day APS3 ACCESS-C forecast C3 and ensemble CE3 models and further development of higher resolution (down to 300 m) fire weather and urban models.

Verification and Assessment of Real-time Forecasts of Two Extreme Heavy Rain Events in Zhengzhou by Operational NWP Models

Verification and Assessment of Real-time Forecasts of Two Extreme Heavy Rain Events in Zhengzhou by Operational NWP Models

Adaptation of the Predicted Particles Properties (P3) Microphysics Scheme for Large-Scale Numerical Weather Prediction

AbstractA parameterization for the subgrid-scale cloud and precipitation fractions has been incorporated into the Predicted Particle Properties (P3) microphysics scheme for use in atmospheric models with relatively coarse horizontal resolution. The modified scheme was tested in a simple 1D kinematic model and in the Canadian Global Environmental Multiscale (GEM) model using an operational global NWP configuration with a 25-km grid spacing. A series of 5-day forecast simulations was run using P3 and the much simpler operational Sundqvist condensation scheme as a benchmark for comparison. The effects of using P3 in a global GEM configuration, with and without the modifications, were explored through statistical metrics of common forecast fields against upper-air and surface observations. Diagnostics of state variable tendencies from various physics parameterizations were examined to identify possible sources of errors resulting from the use of the modified scheme. Sensitivity tests were performed on the coupling between the deep convection parameterization scheme and the microphysics, specifically regarding assumptions in the physical properties of detrained ice. It was found that even without recalibration of the suite of moist physical parameterizations, substituting the Sundqvist condensation scheme with the modified P3 microphysics resulted in some significant improvements to the temperature and geopotential height bias throughout the troposphere and out to day 5, but with degradation to error standard deviation toward the end of the integrations, as well as an increase in the positive bias of precipitation quantities. The modified P3 scheme was thus shown to hold promise for potential use in coarse-resolution NWP systems.

Local Ensemble Transform Kalman Filter with Cross Validation

Abstract Many ensemble data assimilation (DA) approaches suffer from the so-called inbreeding problem. As a consequence, there is an excessive reduction in ensemble spread by the DA procedure, causing the analysis ensemble spread to systematically underestimate the uncertainty of the ensemble mean analysis. The stochastic EnKF used for operational NWP in Canada largely avoids this problem by applying cross validation, that is, using an independent subset of ensemble members for updating each member. The goal of the present study is to evaluate two new variations of the local ensemble transform Kalman filter (LETKF) that also incorporate cross validation. In idealized numerical experiments with Gaussian-distributed background ensembles, the two new LETKF approaches are shown to produce reliable analysis ensembles such that the ensemble spread closely matches the uncertainty of the ensemble mean, without any ensemble inflation. In ensemble DA experiments with highly nonlinear idealized forecast models, the deterministic version of the LETKF with cross validation quickly diverges, but the stochastic version produces better results, nearly identical to the stochastic EnKF with cross validation. In the context of a regional NWP system, ensemble DA experiments are performed with the two new LETKF-based approaches with cross validation, the standard LETKF, and the stochastic EnKF. All approaches with cross validation produce similar ensemble spread at the first analysis time, though the amplitude of the changes to the individual members is larger with the stochastic approaches. Over the 10-day period of the experiments, the fit of the ensemble mean background state to radiosonde observations is statistically indistinguishable for all approaches evaluated.

On the probabilistic skill of dual‐resolution ensemble forecasts

AbstractIncreasing spatial resolution and increasing ensemble size both tend to improve the skill of ensemble forecasts. Due to computational constraints, a balance needs to be found in operational NWP. Here, we examine a scenario where ensembles are formed by pooling lower‐resolution and higher‐resolution members such that the overall computational cost is equal to the constraint. The approach is applied to medium‐range weather forecasts with ECMWF's Integrated Forecasting System using horizontal resolutions of 18, 29 and 45 km and ensemble sizes ranging from 8 to 254 members. The methodology is similar to the multi‐level Monte‐Carlo approach but does not use stochastic perturbations that are shared between members at different levels. Probabilistic skill is quantified for 850 hPa temperature verified against analyses and 2 m temperature verified against station observations. Generally, dual‐resolution ensembles with similar numbers of lower and higher‐resolution members provide the optimal configuration for 2 m temperature prediction. In contrast, single‐resolution ensembles appear to be the most skilful for 850 hPa temperature. The dual‐resolution ensembles are a special kind of multi‐model ensemble. An analytic model describing the skill of such a multi‐model ensemble is developed and its parameters are estimated from the actual verification statistics. The model is capable of describing the general differences in behaviour between 2 m temperature and 850 hPa temperature.

Tropical Cyclone Forecasts Impact Assessment from the Assimilation of Hourly Visible, Shortwave, and Clear-Air Water Vapor Atmospheric Motion Vectors in HWRF

Abstract The assimilation of atmospheric motion vectors (AMVs) provides important wind information to conventional data-lacking oceanic regions, where tropical cyclones spend most of their lifetimes. Three new AMV types, shortwave infrared (SWIR), clear-air water vapor (CAWV), and visible (VIS), are produced hourly by NOAA/NESDIS and are assimilated in operational NWP systems. The new AMV data types are added to the hourly infrared (IR) and cloud-top water vapor (CTWV) AMV data in the 2016 operational version of the HWRF Model. In this study, we update existing quality control (QC) procedures and add new procedures specific to tropical cyclone assimilation. We assess the impact of the three new AMV types on tropical cyclone forecasts by conducting assimilation experiments for 25 Atlantic tropical cyclones from the 2015 and 2016 hurricane seasons. Forecasts are analyzed by considering all tropical cyclones as a group and classifying them into strong/weak storm vortices based on their initial model intensity. Metrics such as track error, intensity error, minimum central pressure error, and storm size are used to assess the data impact from the addition of the three new AMV types. Positive impact is obtained for these metrics, indicating that assimilating SWIR-, CAWV-, and VIS-type AMVs are beneficial for tropical cyclone forecasting. Given the results presented here, the new AMV types were accepted into NOAA/NCEP’s operational HWRF for the 2017 hurricane season.

A Localized Adaptive Particle Filter within an Operational NWP Framework

Particle filters are well known in statistics. They have a long tradition in the framework of ensemble data assimilation (EDA) as well as Markov chain Monte Carlo (MCMC) methods. A key challenge today is to employ such methods in a high-dimensional environment, since the naïve application of the classical particle filter usually leads to filter divergence or filter collapse when applied within the very high dimension of many practical assimilation problems (known as the curse of dimensionality). The goal of this work is to develop a localized adaptive particle filter (LAPF), which follows closely the idea of the classical MCMC or bootstrap-type particle filter, but overcomes the problems of collapse and divergence based on localization in the spirit of the local ensemble transform Kalman filter (LETKF) and adaptivity with an adaptive Gaussian resampling or rejuvenation scheme in ensemble space. The particle filter has been implemented in the data assimilation system for the global forecast model ICON at Deutscher Wetterdienst (DWD). We carry out simulations over a period of 1 month with a global horizontal resolution of 52 km and 90 layers. With four variables analyzed per grid point, this leads to 6.6 × 106 degrees of freedom. The LAPF can be run stably and shows a reasonable performance. We compare its scores to the operational setup of the ICON LETKF.

A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

Abstract Two types of approaches are commonly used for estimating the impact of arbitrary subsets of observations on short-range forecast error. The first was developed for variational data assimilation systems and requires the adjoint of the forecast model. Comparable approaches were developed for use with the ensemble Kalman filter and rely on ensembles of forecasts. In this study, a new approach for computing observation impact is proposed for ensemble–variational data assimilation (EnVar). Like standard adjoint approaches, the adjoint of the data assimilation procedure is implemented through the iterative minimization of a modified cost function. However, like ensemble approaches, the adjoint of the forecast step is obtained by using an ensemble of forecasts. Numerical experiments were performed to compare the new approach with the standard adjoint approach in the context of operational deterministic NWP. Generally similar results are obtained with both approaches, especially when the new approach uses covariance localization that is horizontally advected between analysis and forecast times. However, large differences in estimated impacts are obtained for some surface observations. Vertical propagation of the observation impact is noticeably restricted with the new approach because of vertical covariance localization. The new approach is used to evaluate changes in observation impact as a result of the use of interchannel observation error correlations for radiance observations. The estimated observation impact in similarly configured global and regional prediction systems is also compared. Overall, the new approach should provide useful estimates of observation impact for data assimilation systems based on EnVar when an adjoint model is not available.

Proactive QC: A Fully Flow-Dependent Quality Control Scheme Based on EFSO

AbstractDespite dramatic improvements over the last decades, operational NWP forecasts still occasionally suffer from abrupt drops in their forecast skill. Such forecast skill “dropouts” may occur even in a perfect NWP system because of the stochastic nature of NWP but can also result from flaws in the NWP system. Recent studies have shown that dropouts occur due not to a model’s deficiencies but to misspecified initial conditions, suggesting that they could be mitigated by improving the quality control (QC) system so that the observation-minus-background (O-B) innovations that would degrade a forecast can be detected and rejected. The ensemble forecast sensitivity to observations (EFSO) technique enables for the quantification of how much each observation has improved or degraded the forecast. A recent study has shown that 24-h EFSO can detect detrimental O-B innovations that caused regional forecast skill dropouts and that the forecast can be improved by not assimilating them. Inspired by that success, a new QC method is proposed, termed proactive QC (PQC), that detects detrimental innovations 6 h after the analysis using EFSO and then repeats the analysis and forecast without using them. PQC is implemented and tested on a lower-resolution version of NCEP’s operational global NWP system. It is shown that EFSO is insensitive to the choice of verification and lead time (24 or 6 h) and that PQC likely improves the analysis, as attested to by forecast improvements of up to 5 days and beyond. Strategies for reducing the computational costs and further optimizing the observation rejection criteria are also discussed.

Toward Scatterometer Winds Assimilation in the Mesoscale HARMONIE Model

Data assimilation (DA) experiments have been conducted with the high-resolution limited-area model HirLAM Aladin Regional Mesoscale Operational NWP In Euromed (HARMONIE), which is operational at most weather centers, which are part of the European HirLAM consortium. Recently, the assimilation of scatterometer ocean surface winds was introduced, showing limited forecast skill improvement. Possible explanations are discussed. These include model bias and the time mismatch between observation and analysis time, which introduces nonnegligible correlated errors in a three-dimensional (3-D) variational assimilation system. Also, ignoring the time mismatch increases the innovation, i.e., the observation minus background (model short-term forecast), by about 20% for scatterometer winds. The use of observations as point observations in most DA systems needs reconsideration for mesoscale DA. The introduction of observation operators, taking into account the instrument footprint, would improve the innovation by about 5% for scatterometer winds. Additional directions for improved use of observations in HARMONIE are discussed based on the notice that DA is an inherent deterministic concept. Hence, the selection of the spatial scale for deterministic DA should depend primarily on the 4-D observation coverage rather than the effective model resolution.

Damaging Southerly Winds Caused by Barrier Jets in the Cook Strait and Wellington Region of New Zealand

Abstract Strong southerly winds regularly occur in the Cook Strait region of New Zealand. Occasionally, these winds are strong enough to cause severe damage to property and threaten human life. One example of a storm containing such winds is the “Wellington Storm,” which occurred on 20 June 2013. For this case, wind speeds in Cook Strait were stronger than those observed or forecast elsewhere in the storm. Even though wind speeds of this intensity are rare, storms affecting New Zealand with central pressures equal to the Wellington Storm (~976 hPa) are not uncommon. Numerical experiments have been carried out to investigate the possible reasons for the exceptional damaging southerly winds (DSWs) occurring in this storm. Analyses of the simulations showed that DSWs in Cook Strait for this event were actually barrier jets, not gap winds as they appeared. The strength of barrier jets in Cook Strait is sensitive to the precise location of the storm center. This explains the uncommon occurrence of DSWs in Cook Strait. Numerical experiments that used scaled (either increased or decreased) New Zealand orography showed that the barrier jets became shallower and weaker when the mountain top heights were lower. This decrease in barrier jet strength with mountain height is largely consistent with the results from linear-scale analyses in previous publications. This result implies that numerical simulations using a lower topography than actual (usually the case in current operational NWP) may lead to errors in timing and in forecasting the strength of the damaging winds associated with barrier jets.

Ensemble Prediction with Different Spatial Resolutions for the 2014 Sochi Winter Olympic Games: The Effects of Calibration and Multimodel Approaches

Abstract Three ensemble prediction systems (EPSs) with different grid spacings are compared and evaluated with respect to their ability to predict wintertime weather in complex terrain. The experiment period was two-and-a-half winter months in 2014, coinciding with the Forecast and Research in the Olympic Sochi Testbed (FROST) project, which took place during the Winter Olympic Games in Sochi, Russia. The global, synoptic-scale ensemble system used is the IFS ENS from the European Centre for Medium-Range Weather Forecasts (ECMWF), and its performance is compared with both the operational pan-European Grand Limited Area Ensemble Prediction System (GLAMEPS) at 11-km horizontal resolution and the experimental regional convection-permitting HIRLAM–ALADIN Regional Mesoscale Operational NWP in Europe (HARMONIE) EPS (HarmonEPS) at 2.5 km. Both GLAMEPS and HarmonEPS are multimodel systems, and it is seen that a large part of the skill in these systems comes from the multimodel approach, as long as all subensembles are performing reasonably. The number of members has less impact on the overall skill measurement. The relative importance of resolution and calibration is also assessed. Statistical calibration was applied and evaluated. In contrast to what is seen for the raw ensembles, the number of members, as well as the number of subensembles, is important for the calibrated ensembles. HarmonEPS shows greater potential than GLAMEPS for predicting wintertime weather, and also has an advantage after calibration.

Observing Boundary-Layer Winds from Hot-Air Balloon Flights

Abstract High-resolution upper-air wind observations are sparse, and additional observations are a welcome source of meteorological information. In this paper the potential of applying balloon flights for upper-air wind measurements is explored, and the meteorological content of this information is investigated. The displacement of a hot-air balloon is a measure for the wind speed and direction and thus a potential source for wind observations in the lower part of the troposphere. The response time of the balloon on the changing wind is fast in the beginning and levels off for smaller relative wind speeds. Four case studies are presented, and the balloon-derived winds are compared with other wind observations and with results from the HIRLAM–ALADIN Research on Mesoscale Operational NWP in Europe (HARMONIE) model. It turns out that hot-air balloon tracks can indeed produce useful wind observations just above and in the atmospheric boundary layer (ABL).

50th anniversary of Numerical Weather Prediction (NWP) in the UK

50th anniversary of Numerical Weather Prediction (<scp>NWP</scp>) in the <scp>UK</scp>