History of Downscaling

A new parametrization for the surface energy balance of urban areas is presented. It is shown that this new method can represent some of the important urban phenomena, such as an urban heat island and the occurrence of a near-neutral nocturnal boundary layer with associated positive turbulent heat fluxes, unlike the traditional method for representing urban areas within operational numerical weather prediction (NWP) models. The basis of the new parametrization is simple and can be applied easily within an operational NWP model. Also, it has no additional computational expense compared to the traditional scheme and is hence applicable for operational forecasting requirements. The results show that the errors for London within the Met Office operational mesoscale model have been significantly reduced since the new scheme was introduced. The bias and root-mean-square (rms) errors have been approximately halved, with the rms error now similar to the model as a whole. The results also show that a seasonal cycle still exists in the model errors, but it is suggested that this may be caused by anthropogenic heat sources that are neglected in the urban scheme.

The ocean and the atmosphere are coupled by the fluxes of momentum, heat, and water, but in situ measurements of these fluxes are sparse and uneven. Global fields of these fluxes can be derived from satellite data or obtained from operational numerical weather prediction (NWP) models. The NWP models provide quality control and dynamic interpolation of sparse and uneven data. However, dynamic interpolation is only as good as the model physics and parameterization scheme, and over most of the ocean, there may not be sufficient data for any useful interpolation. Operational NWP models change frequently; the results are inconsistent of climatic studies. Reanalysis to produce long period of consistent data have only been started recently. Adequate observations at temporal and spatial scales significant to global change research can only be achieved from the vantage point of space. Spaceborne sensors provide repeated global observations of some parameters from which these fluxes can be derived. The status and shortfalls of spacebased estimation of the fluxes are summarized and assessed in this report. Satellite data can be assimilated into numerical model to simulate surface fluxes, but the progress in developing assimilation techniques for unconventional satellite data has been slow.

Master optimization process based on neural networks ensemble for 24-h solar irradiance forecast

Artificial intelligence and numerical weather prediction models: A technical survey

The global navigation satellite system (GNSS) can provide centimeter positioning accuracy at low costs. However, in order to obtain the desired high accuracy, it is necessary to use high-quality atmospheric models. We focus on the troposphere, which is an important topic of research in Brazil where the tropospheric characteristics are unique, both spatially and temporally. There are dry regions, which lie mainly in the central part of the country. However, the most interesting area for the investigation of tropospheric models is the wet region which is located in the Amazon forest. This region substantially affects the variability of humidity over other regions of Brazil. It provides a large quantity of water vapor through the humidity convergence zone, especially for the southeast region. The interconnection and large fluxes of water vapor can generate serious deficiencies in tropospheric modeling. The CPTEC/INPE (Center for Weather Forecasting and Climate Studies/Brazilian Institute for Space Research) has been providing since July 2012 a numerical weather prediction (NWP) model for South America, known as Eta. It has yield excellent results in weather prediction but has not been used in GNSS positioning. This NWP model was evaluated in precise point positioning (PPP) and network-based positioning. Concerning PPP, the best positioning results were obtained for the station SAGA, located in Amazon region. Using the NWP model, the 3D RMS are less than 10 cm for all 24 h of data, whereas the values reach approximately 60 cm for the Hopfield model. For network-based positioning, the best results were obtained mainly when the tropospheric characteristics are critical, in which case an improvement of up to 7.2 % was obtained in 3D RMS using NWP models.

Improving the Turbulence Coupling between High Resolution Numerical Weather Prediction Models and Lagrangian Particle Dispersion Models

Optimized advection of radar reflectivities

Accurate wind speed forecasting is essential for daily activities and socio‐economic production. This study employs an explainable machine learning (ML) model to correct wind speed forecasts from a numerical weather prediction (NWP) model over weather stations across lead times of 1–24 hr in Zhejiang Province, China. Eleven meteorological variables derived from the NWP model are used as predictors. The results demonstrate that ML model substantially improves the original NWP forecasts, reducing the root mean square error of approximately 20%–40% at most stations and about 10% at certain coastal stations. The corrected forecasts also consistently achieve higher Kling‐Gupta Efficiency scores and Forecast Accuracy across all lead times. To enhance model interpretability, we apply Split Count and SHapley Additive exPlanation analyses. The results indicate that the friction velocity (UST) is the most influential predictor and reveal a threshold beyond which the ML model's correction performance improves markedly. The ML model performance slightly declines in coastal regions, likely due to the inconsistent representation of UST between the land and water surface in the NWP model. Monthly analysis further shows superior model performance in November and December, when UST values are higher. This study underscores the value of explainable ML models in improving NWP wind forecasts and provides insights for correcting meteorological variables in NWP models.

Abstract. The FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) Mission consisting of six Low-Earth-Orbit (LEO) satellites is the world's first demonstration constellation using radio occultation signals from Global Positioning System (GPS) satellites. The atmospheric profiles derived by processing radio occultation signals are retrieved in near real-time for global weather/climate monitoring, numerical weather prediction, and space weather research. The mission has processed, on average, 1400 to 1800 high-quality atmospheric sounding profiles per day. The atmospheric radio occultation data are assimilated into operational numerical weather prediction models for global weather prediction, including typhoon/hurricane/cyclone forecasts. The radio occultation data has shown a positive impact on weather predictions at many national weather forecast centers. A follow-on mission was proposed that transitions the current experimental research mission into a significantly improved real-time operational mission, which will reliably provide 8000 radio occultation soundings per day. The follow-on mission, as planned, will consist of 12 LEO satellites (compared to 6 satellites for the current mission) with data latency requirement of 45 min (compared to 3 h for the current mission), which will provide greatly enhanced opportunities for operational forecasts and scientific research. This paper will address the FORMOSAT-3/COSMIC system and mission overview, the spacecraft and ground system performance after four years in orbit, the lessons learned from the encountered technical challenges and observations, and the expected design improvements for the spacecraft and ground system for FORMOSAT-7/COSMIC-2.

A semi-Lagrangian numerical weather prediction (NWP) model developed for both real-time prediction and for research simulations has been evaluated. The model is second-order in time, high-order (≥ 3) in space, and employs a non-staggered grid in both the horizontal and vertical. A version of the model which is third-order in space was compared with two Eulerian models: the Australian Bureau of Meteorology's current operational regional model which is quasi-second-order in rime and space, and also a new version of this model with a third-order upwind scheme that was developed for use as the Australian Bureau of Meteorology's next operational limited-area NWP model. In a three month trial of twice-daily 48-hour forecasts it was found that both the third-order models were significantly more skillful than the current operational model, as measured by the standard performance statistics such as the S1 skill score (Teweles and Wobus, 1954), and root-mean-square (RMS) errors. The specific implications of this greater accuracy were examined in case studies of severe weather events. The semi-Lagrangian model also bas been adapted to global form and run on a daily basis out to 5 days using archived operational data over a period of almost 6 months. Finally, the semi-Lagrangian model code was parallelized on a workstation cluster and also on a scalable parallel computer, and it was found that the model was well-suited to parallelization on both computer platforms.

This summary, covering the three-month period from January to March 2007, continues the series reporting on the performances of numerical weather prediction (NWP) models used operationally in the Australian Bureau of Meteorology.

Machine learning, and particularly neural networks, have been touted as a valuable accelerator for physical processes. By training on data generated from an existing algorithm a network may theoretically learn a more efficient representation and accelerate the computations via emulation. For many parameterized physical processes in weather and climate models this being actively pursued. Here, we examine the value of this approach for radiative transfer within the IFS, an operational numerical weather prediction model where both accuracy and speed are vital. By designing custom, physics-informed, neural networks we achieve outstanding offline accuracy for both longwave and shortwave processes. In coupled testing we find minimal changes to forecast scores at near operational resolutions. We carry out coupled inference on GPUs to maximise the speed benefits from the emulator approach.

The Beginnings of Numerical Weather Prediction and General Circulation Modeling: Early Recollections

Since 1970, tropical cyclone (TC) track forecasts have improved steadily in the Atlantic basin. This improvement has been linked primarily to advances in numerical weather prediction (NWP) models. Concurrently, with few exceptions, the development and operational use of statistical track prediction schemes have experienced a relative decline. Statistical schemes provided the most accurate TC track forecasts until approximately the late 1980s. In this note, it is shown that increased reliance on the global NWP models does not always guarantee the best forecast. Here, Hurricane Ivan is used from the 2004 Atlantic TC season as a classical example, and reminder, of how strong climatological signals still can add substantial value to TC track forecasts, in the form of improved accuracy and increased timeliness at minimal computational cost. In an 8-day period in early September 2004, Hurricane Ivan was repeatedly, and incorrectly, forecast by 12 operational NWP models to move with a significant northward (poleward) component. It was found that the mean 24-h trajectory forecasts of a consensus of five commonly used NWP track prediction aids had a statistically significant right-of-track bias. Furthermore, the official track forecasts, which relied heavily on erroneous numerical guidance over this period, were also found to have significant poleward trajectory errors. At the same time, a climatology-based prediction technique, drawn entirely from the historical record of motion characteristics of TCs in geographical locations similar to Ivan, correctly and consistently indicated a more westward motion component, had a small directional spread, and was supported by a large number of archived cases. This climatological signal was in conflict with the deterministic NWP model output, and it is suggested that the large errors in the official track forecast for TC Ivan could have been reduced considerably by taking into greater account such a strong climatological signal. The potential impact of such an error reduction is a saving of lives and billions of dollars in both actual damage and unnecessary evacuations costs, for just this one hurricane. We also suggest that this simple strategy of examining the strength of the climatological signal be considered for all TCs to identify cases where the NWP and official forecasts differ significantly from strong, persistent climatological signals.

The potential benefit of geostationary satellite radiance observations, taken in clear-sky and cloudy atmospheric conditions, on the analysis and forecast accuracy of a numerical weather prediction (NWP) model was investigated. The NWP model used is the High Resolution Limited Area Model (HIRLAM) which provides with its state-of-the-art four-dimensional variational data assimilation (4D-Var) scheme an optimal framework to exploit spatio-temporal highly resolved geostationary infrared radiance observations such as measured by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on-board the METEOSAT Second Generation satellite series. In the framework of this work the assimilation scheme has firstly been modified to enable the SEVIRI radiance observations being used as additional observation type, and secondly, it has been equipped with a observation operator for satellite radiances particularly designed for those radiances significantly affected by clouds. For the latter, a simplified moist physics scheme was implemented to diagnose the cloudiness from the NWP background state valid at the time of the observation. Sophisticated observation screening procedures have additionally been developed and implemented to provide both the identification of cloud- unaffected radiances in clear-sky regions and low-level cloud regimes, and the identification of usable cloud-affected radiances by matching of modelled and observed cloudiness. Further, the developed extended assimilation scheme has been tested and evaluated in various comprehensive assimilation and forecast experiments. The forecast accuracy was assessed by comparison with standard conventional observations, which were valid at the same time, as reference. The geostationary satellite observations in clear-sky regions were found to have a small positive impact on the HIRLAM forecast accuracy, in particular on the upper air values of temperature, relative humidity, geopotential height and wind. Adding radiance observations in the presence of low-level clouds further improves the accuracy mostly for geopotential height and precipitation. Cloud-affected radiances assimilated on top of the clear-sky radiances also alter the analysis fields. Corresponding forecasts were characterized by improvements in the mid and upper troposphere. Minor, but visible degradations for temperature and humidity forecasts were found in the lower troposphere. Overall, the impact of assimilated cloud-affected radiances on the forecast quality, given the described framework, is judged to be slightly positive. The results clearly indicate the positive impact that geostationary satellite observations of SEVIRI-like instruments can have on the accuracy of a limited-area NWP model with the given framework. In particular the inclusion of the time dimension in the 4D-Var system might be the key to fully explore such high-frequent satellite observations. The developed framework further provides a tool to employ satellite observations, also from other sensors, in nearly all atmospheric conditions. The full potential will be revealed when applied on global scale.