Global Numerical Weather Prediction Model Research Articles

Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)

Abstract. This article presents a description of an analytical, stable, and flexible initial background state for both dry and moist baroclinic wave simulation on an aquaplanet in order to test the dynamical core of numerical weather prediction models and study the dynamics and evolution of extratropical cyclones. The initial background state is derived from an analytical zonal wind speed field, or jet structure, and the hydrostatic primitive equations for moist adiabatic and frictionless flow in spherical coordinates. A baroclinic wave can develop if a perturbation is added to the zonal wind speed field. This new baroclinic wave configuration has been implemented in the Open Integrated Forecasting System (OpenIFS) CY43R3, a global numerical weather prediction model developed by the European Centre for Medium-Range Weather Forecasts. In total, seven parameters can be used to control the generation of the initial background state and hence the development of the baroclinic waves in the OpenIFS configuration file: the jet's width, the jet's height, the maximum zonal mean wind speed of the jet, the horizontal mean of the surface virtual temperature, the surface relative humidity, the lapse rate, and the surface roughness. Nine dry and nine moist initial background states have been generated to test their stability without perturbations. The meteorological stability of the initial states is investigated by examining the spatial distributions of the equivalent potential temperature, the absolute vorticity, and the Brunt–Väisälä frequency. Moreover, the root mean square error (RMSE) of the zonal wind speed has been computed to assess their numerical stability. Finally, six dry and six moist initial background state have been used with an unbalanced perturbation to ensure that the baroclinic life cycles that develop are physically realistic. The resulting baroclinic wave is shown to be sensitive to the jet's width. This configuration for baroclinic wave simulations will be used to create a large ensemble of baroclinic life cycles to study how extratropical cyclones may evolve in the future.

Weather‐Induced Satellite Orbit Perturbations

AbstractSatellites in Earth's orbit are exposed to Earth radiation, consisting of reflected solar and emitted thermal radiation, thereby exerting a non‐conservative force that causes acceleration and affects the orbits. Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) mission aiming to retrieve the Earth's gravity potential is critically dependent on accounting for this force and all other non‐gravitational forces. There are both diurnal and seasonal variations in the Earth's radiation pressure, of which the seasonal variability can be represented by climatology. Nevertheless, the daily variations in the Earth's radiation pressure, due to the transient changes in the weather; for example, clouds and their properties, are not accounted for in the orbit perturbations studies. We show here that the top‐of‐atmosphere radiation fluxes computed with a numerical weather prediction (NWP) model explain most of the measured short‐term variations in the radial acceleration of the GRACE‐FO satellite. Our physics‐based modeling corrects a hitherto unexplained lack of power spectral density in the measured accelerations. For example, we can accurately model the accelerations associated with a tropical storm in the Indian Ocean in December 2020, which would not be possible when using climatological data. Our results demonstrate that using a global numerical weather prediction model significantly improves the simulation of non‐gravitational effects in the satellites' orbits. In the 7‐day data set, OpenIFS‐simulated acceleration exhibited higher accuracy than climatological‐data‐simulated acceleration (2.5 compared to 2.6 nms−2) and an improved precision (2.6 compared to 3.0 nms−2). This advancement contributes to a more precise orbit determination across various applications in Earth sciences.

Multi‐Platform Observations of Severe Typhoon Koinu

AbstractSevere Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issuance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone (TC) warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over the coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and dropsonde data suggested boundary layer inflow, warm core structure, eyewall updraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated the occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study highlights the forecasting performance of the AI‐based Pangu‐Weather model, which could outperform the conventional global numerical weather prediction models in forecasting TC track in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclone.

Operational Aviation Icing Forecast Algorithm for the Korea Meteorological Administration

Abstract In this study, we developed and evaluated the Korean Forecast Icing Potential (K-FIP), an in-flight icing forecast system for the Korea Meteorological Administration (KMA) based on the simplified forecast icing potential (SFIP) algorithm. The SFIP is an algorithm used to postprocess numerical weather prediction (NWP) model forecasts for predicting potential areas of icing based on the fuzzy logic formulations of four membership functions: temperature, relative humidity, vertical velocity, and cloud liquid water content. In this study, we optimized the original version of the SFIP for the global NWP model of the KMA through three important updates using 34 months of pilot reports for icing as follows: using total cloud condensates, reconstructing membership functions, and determining the best weight combination for input variables. The use of all cloud condensates and the reconstruction of these membership functions resulted in a significant improvement in the algorithm compared with the original. The weight combinations for the KMA’s global model were determined based on the performance scores. While several sets of weights performed equally well, this process identified the most effective weight combination for the KMA model, which is referred to as the K-FIP. The K-FIP demonstrated the ability to successfully predict icing over the Korean Peninsula using observations made by research aircraft from the National Institute of Meteorological Sciences of the KMA. Eventually, the K-FIP icing forecasts will provide better forecasts of icing potentials for safe and efficient aviation operations in South Korea. Significance Statement In-flight aircraft icing has posed a threat to safe flights for decades. With advances in computing resources and an improvement in the spatiotemporal resolutions of numerical weather prediction (NWP) models, icing algorithms have been developed using NWP model outputs associated with supercooled liquid water. This study evaluated and optimized the simplified forecast icing potential, an NWP model–based icing algorithm, for the global model of the Korean Meteorological Administration (KMA) using a long-term observational dataset to improve its prediction skills. The improvements shown in this study and the SFIP implemented in the KMA will provide more informative predictions for safe and efficient air travel.

A tolerant hydrologic technique for real-time selection of optimum QPFs from NWPMs for flood warning applications.

The most important information required to successfully issue a flood warning is the quantitative precipitation forecasts (QPFs). This is important to run subsequent rainfall-runoff simulations. A rainfall-runoff simulation derives its accuracy mainly from the accuracy of the input QPFs. The dynamically based global numerical weather prediction models (NWPMs) are strong candidate sources of QPFs. A main problem is the real-time selection of which NWPM should be used to provide the QPFs for flood warning simulations. This paper develops an automated technique to solve this problem. The technique performs real-time comparisons with measured rainfall fields using a novel 'tolerant' hydrologic approach. The 'tolerant' approach performs the comparison on the basin scale and allows for timing shifts in the forecasts. This is because QPFs can be good but only a few hours early or late. Two events are used for illustration, and the proposed real-time application in flood warning is presented. The developed technique, employing the tolerant approach, could eliminate the effects of the timing shifts and, accordingly, succeeded to select the QPFs to be used. A Python package was developed for automation. The developed technique is expected to also be useful for offline assessments of historical performances of NWPMs.

Improving GNSS PPP-RTK through global forecast system zenith wet delay augmentation

The precise point positioning real-time kinematic (PPP-RTK) is a high-precision global navigation satellite system (GNSS) positioning technique that combines the advantages of wide-area coverage in precise point positioning (PPP) and of rapid convergence in real-time kinematic (RTK). However, the PPP-RTK convergence is still limited by the precision of slant ionospheric delays and tropospheric zenith wet delay (ZWD), which affects the PPP-RTK network parameters estimation and user positioning performance. The present study aims to construct a PPP-RTK model augmented with a priori ZWD values derived from the global forecast system (GFS) product (a global numerical weather prediction (NWP) model) to improve the PPP-RTK performance. This study gives a priori ZWD values and conversion based on the GFS products, and the full-rank GFS-augmented undifferenced and uncombined (UDUC) PPP-RTK network model is derived. To verify the performance of GFS-augmented UDUC PPP-RTK, a comprehensive evaluation using 10-day GNSS observation data from three different GNSS station networks in the United States (US), Australia, and Europe is conducted. The results show that with the GFS ZWD a priori information, PPP-RTK performance significantly improves at the initial filtering stage, but this advantage gradually decays over time. Based on 10-day positioning results for all user stations, the GFS ZWD-augmented PPP-RTK approach reduces the average convergence time by 46% from 10.0 to 5.4 min, the three-dimensional root-mean-square (3D-RMS) error by 5.7% from 3.5 to 3.3 cm, and the time to first fix (TTFF) value by 35.8% from 6.7 to 4.3 min, all when compared to the traditional PPP-RTK without GFS ZWD constraints.

Impacts of Dropsonde Observations on Forecasts of Atmospheric Rivers and Associated Precipitation in the NCEP GFS and ECMWF IFS Models

Abstract Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used. Significance Statement Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models.

Doppler LiDAR Observation of Subsidence in Synoptic Scale and Performance of a Global Numerical Weather Prediction Model in Capturing the Subsidence

The vertical velocity data from a Doppler LiDAR situated at the centre of Hong Kong were examined to look for signature of subsidence within the atmospheric boundary layer against a synoptic background. Two case studies were performed, namely, stable atmospheric conditions in foggy weather and possible “subsidence heating” at the periphery of the outer circulation of an intense tropical cyclone. The LiDAR’s Doppler velocity data were found to provide insights into the vertical motion of the air on the synoptic scale. They appear to confirm subsidence in foggy weather but provide new information about the mechanism for the occurrence of extremely hot weather. The data were also compared with vertical velocity forecasts from a numerical weather prediction model to assess the quality of the forecast. The Doppler LiDAR’s vertical velocity data were found to be useful in the verification of omega forecasts from the global numerical weather prediction model. They were found to provide further insights into the subsidence of the troposphere, particularly the atmospheric boundary layer, in certain synoptic patterns.

Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME

Inspecting the literature, much effort has been placed on the verification of irradiance forecasts from numerical weather prediction (NWP) models, as such forecasts are thought to have profound implications on the photovoltaic (PV) power forecasts, which in turn affects grid operators' confidence in integrating such power into the electricity grid. However, perhaps due to the proprietary nature of PV plants and lack of access to state-of-the-art NWP model output, only few have had the chance to conduct head-to-head comparisons of global mesoscale and regional downscaled NWP models, in terms of how their irradiance forecast inaccuracies propagate to PV power forecasts. In this regard, this work presents such a study, in which irradiance and PV power forecasts from the European Centre for Medium-Range Weather Forecasts' High-Resolution (HRES) and Météo-France's Application of Research to Operations at Mesoscale (AROME) models are thoroughly verified against the ground-based measurements from 32 research-grade radiometry stations and 94 actual PV plants in Hungary. A wide range of techniques and case studies concerning verification is herein considered, including variance ratio analysis, Murphy–Winkler decomposition, point-versus-areal verification, and seasonal verification. Despite that the results are too numerous to be summarized in a few sentences, the overarching observation from the verification exercise is that the performance of irradiance forecasts can only be used to infer that of PV power forecasts to a certain extent, which contrasts the conventional wisdom.

The impact of Aeolus winds on near-surface wind forecasts over tropical ocean and high-latitude regions

Abstract. To detect global wind profiles and improve numerical weather prediction (NWP), the European Space Agency (ESA) launched the Aeolus satellite carrying a spaceborne Doppler wind lidar in 2018. After the successful launch, the European Centre for Medium-Range Weather Forecasts (ECMWF) performed the observing system experiments (OSEs) to evaluate the contribution of Aeolus data to NWP. This study aims to assess the impact of Aeolus wind assimilation in the ECMWF model on near-surface (10 m height) wind forecasts over tropical ocean regions by taking buoy measurements for reference and over high-latitude regions by taking weather station data for reference for the year 2020. The assessments were conducted mainly through inter-comparison analysis. The results show that Aeolus data assimilation has a limited impact on sea surface wind forecasts for tropical regions when compared with buoy measurements. For the high-latitude regions in the Northern Hemisphere, Aeolus is able to improve near-surface wind forecasts. This positive impact is more evident as the forecast time step is extended, during the first half year of 2020 and during the winter months. In addition, the v component tends to benefit more from the Aeolus observations than the u component. For the Southern Hemisphere, a few error reductions are observed but exist randomly. Overall, this in situ data-based assessment expands our understanding of the role of Aeolus data assimilation with the global NWP model in predicting near-surface wind for tropical oceans and high-latitude regions.

Adaptation of θ-Based Dynamical Cores for Extension into the Thermosphere Using a Hybrid Virtual Potential Temperature

Abstract The virtual temperature used to model moisture-modified tropospheric dynamics is generalized to include a new thermospheric component. The resulting hybrid virtual potential temperature (HVPT) transitions seamlessly with height, from moist virtual potential temperature (MVPT) in the troposphere, to potential temperature in the stratosphere and mesosphere, to thermospheric virtual potential temperature thereafter. For numerical weather prediction (NWP) models looking to extend into the thermosphere, but still heavily invested in retaining MVPT-based dynamical cores for tropospheric prediction, upgrading to HVPT allows the core to capture critical new aspects of variable composition thermospheric dynamics, while leaving the original MVPT-based tropospheric equations and numerics essentially untouched. In this way, HVPT augmentation can both simplify and streamline extension into the thermosphere at little computational cost beyond the inevitable need for more vertical layers and somewhat smaller time steps. To demonstrate, we upgrade the MVPT-based dynamical core of the Navy global NWP model to HVPT, then test its performance in forecasting analytical globally balanced states containing hot or rapidly heated thermospheres and height-varying gas constants. These tests confirm that HVPT augmentation offers an efficient and effective means of extending MVPT-based NWP models into the thermosphere to accelerate development of future ground-to-space NWP models supporting space weather applications. The related issues of variable gravitational acceleration and shallow-atmosphere approximations are also briefly discussed.

A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region

Surface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air pollutants in a short period of time will affect the temperature prediction skill of NWP models. Thus, the study was carried out to investigate the possible influence of air pollution on the temperature forecast skill based on the operational NWP model over the Beijing–Tianjin–Hebei (BTH) region during January–February 2020. The results show that the average concentrations of PM2.5, SO2, NO2 and CO over the BTH region in February were smaller than those in January by 38.5%, 35.1%, 48.0% and 33.1%, respectively. Simultaneously, the forecast skills for surface temperature in February from both regional (RMAPS, Rapid-refresh Multi-scale Analysis and Prediction System) and global (ECMWF, European Centre for Medium-Range Weather Forecasts) operational NWP models improved markedly compared with that in January. In both models, the underestimation of maximum temperature and the overestimation of minimum temperature in most cities over the BTH region in February were significantly reduced. With the 24 h (24 h) forecast lead time, the RMSE (root mean square error) of BTH daily mean, maximum and minimum temperature prediction in February based on RMAPS were 17.3%, 9.8% and 21.6% lower than that in January, respectively. These are generally consistent with the other statistical indices such as deviation and regression coefficient. As the forecast lead time extended to 48 h and 72 h forecast, the phenomena still existed and were also evident in the ECMWF model. The improvement of temperature forecast skill of NWP models may be attributed to the unexpected dramatical reduction of air pollutants. Less aerosols during the daytime allow more solar radiation reaching the surface and cause a warming in the near-surface temperature, while less aerosols during the nighttime favor the outgoing long-wave radiation and then lead to a cooling near the ground.

Aerosol–cloud–radiation interaction during Saharan dust episodes: the dusty cirrus puzzle

Abstract. Dusty cirrus clouds are extended optically thick cirrocumulus decks that occur during strong mineral dust events. So far they have mostly been documented over Europe associated with dust-infused baroclinic storms. Since today's global numerical weather prediction models neither predict mineral dust distributions nor consider the interaction of dust with cloud microphysics, they cannot simulate this phenomenon. We postulate that the dusty cirrus forms through a mixing instability of moist clean air with drier dusty air. A corresponding sub-grid parameterization is suggested and tested in the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART). Only with the help of this parameterization is ICON-ART able to simulate the formation of the dusty cirrus, which leads to substantial improvements in cloud cover and radiative fluxes compared to simulations without this parameterization. A statistical evaluation over six Saharan dust events with and without observed dusty cirrus shows robust improvements in cloud and radiation scores. The ability to simulate dusty cirrus formation removes the linear dependency on mineral dust aerosol optical depth from the bias of the radiative fluxes. For the six Saharan dust episodes investigated in this study, the formation of dusty cirrus clouds is the dominant aerosol–cloud–radiation effect of mineral dust over Europe.

Three-Dimensional Structure of Convectively Coupled Equatorial Waves in Aquaplanet Experiments with Resolved or Parameterized Convection

Abstract Accurate simulations of convectively coupled equatorial waves (CCEWs) are key to properly forecasting rainfall and weather patterns within (and outside) the tropics. Many studies have shown that global numerical weather prediction (NWP) models usually do not accurately simulate CCEWs; however, it is unclear if this problem can be alleviated with a better representation of deep convection in the models. To this end, this study investigates the representation of multiple types of CCEWs in the Model for Prediction Across Scales-Atmosphere (MPAS-A). The simulated structure of CCEWs is analyzed from three MPAS-A aquaplanet experiments with horizontal cell spacing of 30, 15, and 3 km, respectively. Using a wave-phase composite technique, the simulated structure is compared against observed CCEWs as represented by satellite and reanalysis data. All aquaplanet experiments capture the overall structure of gravity wave–type equatorial waves (e.g., Kelvin waves and inertio-gravity waves). Those waves are more realistic in the 3-km experiment, particularly in terms of the vertical structure of temperature, water vapor, and wind anomalies associated with the waves. The main reason for this improvement is a more realistic diabatic heating profile; the experiment with resolved convection produces stronger heating (or weaker cooling) below the melting level during the convectively active phase of Kelvin and inertio-gravity waves. Intriguingly, the rainfall and lower-tropospheric structure associated with easterly waves show pronounced discrepancies between the aquaplanet experiments and reanalysis. Resolved deep convection primarily affects the intensity and propagation speeds of these waves.

Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting

Under the two-step framework of photovoltaic (PV) power forecasting, that is, forecasting first the irradiance and then converting it to PV power, there are two chief ways in which one can account for the uncertainty embedded in the final PV power forecast. One of those is to produce probabilistic irradiance forecast through, for example, ensemble numerical weather prediction (NWP), and the other is to pass the irradiance forecast through a collection of different irradiance-to-power conversion sequences, which are known as model chains. This work investigates, for the first time, into the question: Whether pairing ensemble NWP with ensemble model chain is better than leveraging any individual method alone? Using data from 14 utility-scale ground-mounted PV plants in Hungary and the state-of-the-art global mesoscale NWP model of the European Centre for Medium-Range Weather Forecasts, it is herein demonstrated that the best probabilistic PV power forecast needs to consider both ensemble NWP and ensemble model chain. Furthermore, owing to the higher-quality probabilistic forecasts, the point forecast accuracy is also improved substantially through pairing. Overall, the recommended paring strategy achieves a mean-normalized continuous ranked probability score and a root mean square error of 18.4% and 42.1%, respectively.

Global Observing System Experiments within the Météo-France 4D-Var Data Assimilation System

Abstract Observing system experiments were undertaken within the 4D-Var data assimilation of the Météo-France global numerical weather prediction (NWP) model. A 6-month period was chosen (October 2019–March 2020) where 40 million observations per day were assimilated. The importance of in situ observations provided by aircraft, radiosondes, and surface weather stations, despite their small fractional amount (7%), has been confirmed particularly in the Northern Hemisphere. Moreover, the largest impact over Europe in terms of root-mean-square error (RMSE) scores comes from surface observations. Satellite data play a dominant role over tropical regions and the Southern Hemisphere. Microwave radiances have a more pronounced impact on the long range and on the humidity field than infrared radiances, despite being less numerous (10% versus 80%). Bending angles impact significantly the quality of the upper-troposphere–lower-stratosphere temperature of the tropics and Southern Hemisphere. Atmospheric motion vectors (AMVs) are beneficial in wind forecasts at low and high levels in the tropics and the Southern Hemisphere, but also in the humidity field. Such impacts are only significant during the first 48 h of the forecasts. Scatterometer winds have an impact restricted to low levels that is kept at longer ranges. A comparison with forecast sensitivity–observation impact studies over a 3-month period using the same measure of short-range (24-h) forecast errors reveals that the ranking between the major observing systems is kept between these two ways of measuring observation impact in NWP. From our conclusions, recommendations are provided on possible evolutions of the global observing system for NWP.

Spatial connections in extreme precipitation events obtained from NWP forecasts: A complex network approach

Understanding the spatial variability of extreme precipitation events (EPEs) has always been a challenging task, with climate change further complicating the issue. Many studies have looked into the spatiotemporal characteristics of EPEs and the possibility of them being connected. Most of these studies are based on observation data products which inform about the events after they have occurred. Various Quantitative Precipitation Forecasts (QPFs) obtained from the Numerical Weather Prediction (NWP) models such as those of the European Centre for Medium-range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centre for Medium-Range Weather Forecasting (NCMRWF), and UK Met Office (UKMO) are available which inform about the precipitation events before they have occurred. In this study, we inter-evaluate four gridded (deterministic) QPFs over the Ganga River basin of India for their ability to detect the spatial connections among EPEs during the monsoon months, i.e., June, July, August and September (JJAS). Several experimental runs are performed in multiple time periods (13, 6 and 4 years) at various percentile thresholds (85th, 90th and 95th percentile) at two spatial resolutions of 25 and 50-km. Moreover, we also evaluate the forecast patterns of the QPF obtained from the national agency of India, i.e., NCMRWF and analyse its performance with respect to the other three selected QPFs from international agencies. For the observation/reference dataset, the gridded Indian Meteorological Department (IMD) dataset is selected. We first use the deterministic and dichotomous error statistics to evaluate the performance of NWP forecasts. Further, we employ the theory of complex networks to create networks of extreme precipitation at each grid. With this work, we aim to answer (a) can the theory of complex networks be a feasible tool for evaluating the performance of NWP forecasts in detecting extreme precipitation behaviour; (b) does the change in temporal length of the NWP forecasts influence the spatial structure of extreme precipitation as observed before, and (c) can a global NWP model, larger in length, be used as a potential substitute to the NCMRWF model. The ECMWF model performs well with respect to IMD as the average CC for TP_13 is 0.42 while they are 0.31 and 0.34 for TP_6 and TP_4, respectively. Also, we find that ECMWF can be a suitable substitute for the NCMRWF model. The identification of a substitute precipitation forecast, larger in temporal length, can be beneficial as input in hydrological models for streamflow forecasting and estimating parameters for bias correction of precipitation, among others.

Improved Delineation of Urban Hydrological Processes in Coupled Regional Climate Models

AbstractAs the resolution of global and regional numerical weather prediction (NWP) models increases, urban parameterizations are increasingly important because urbanization has significant impacts on the surface energy and water balance. However, NWP models tend to fail to accurately simulate the surface latent heat flux, with most urban canopy models lacking or oversimplifying urban hydrological and vegetation processes. In this study, we develop an urban hydrology scheme and couple it with WRF/BEP + BEM, which improves the regional model performance in urban areas at high resolution. The evaluation results show improved model accuracy not only for the surface latent heat flux but also for the air temperature and humidity. The vertical section analysis suggests that the urban hydrology scheme can have impacts on the boundary layer to a height of 1,000 m. Consequently, improved precipitation simulation causes reasonable prediction of urban surface water depth, which is valuable for early warning of waterlogging.

Global Distributions of Tropospheric and Stratospheric Gravity Wave Momentum Fluxes Resolved by the 9-km ECMWF Experiments

Abstract Based on 20-day control forecasts by the 9-km Integrated Forecasting System (IFS) at the European Centre for Medium-Range Weather Forecasts (ECMWF) for selected periods of summer and winter events, this study investigates global distributions of gravity wave momentum fluxes resolved by the highest-resolution-ever global operational numerical weather prediction model. Two supplementary datasets, including 18-km ECMWF IFS experiments and the 30-km ERA5, are included for comparison. In the stratosphere, there is a clear dominance of westward momentum fluxes over the winter extratropics with strong baroclinic instability, while eastward momentum fluxes are found in the summer tropics. However, meridional momentum fluxes, locally as important as the above zonal counterpart, show different behaviors of global distribution characteristics, with northward and southward momentum fluxes alternating with each other especially at lower altitudes. Both events illustrate conclusive evidence that stronger stratospheric fluxes are found in the ECMWF forecast with finer resolution, and that ERA5 datasets have the weakest signals in general, regardless of whether regridding is applied. In the troposphere, probability distributions of vertical motion perturbations are highly asymmetric with more strong positive signals especially over latitudes covering heavy rainfall, likely caused by convective forcing. With the aid of precipitation accumulation, a simple filtering method is proposed in an attempt to eliminate those tropospheric asymmetries by convective forcing, before calculating tropospheric wave-induced fluxes. Furthermore, this research demonstrates promising findings that the proposed filtering method could help in reducing the potential uncertainties with respect to estimating tropospheric wave-induced fluxes. Finally, absolute momentum flux distributions with proposed approaches are presented, for further assessment in the future.

Forecasting extreme heat in the UK during July 2022

The heatwave that impacted the UK in mid-July 2022 was not only remarkable for the extent and intensity of the heat, but also for how well it was forecast several weeks in advance. In this short article, we analyse the performance of the GEFS, a global numerical weather prediction model run by the US National Centers for Environmental Prediction, highlighting its ability to forecast 40°C temperatures in the UK several weeks in advance.