Convection-permitting models (CPMs) have provided weather forecasting centres with a step-change in capabilities for forecasting rainfall. They are now used operationally to forecast precipitation in many parts of the world, including the UK. CPMs are models in which the dynamics of atmospheric convection is treated with sufficient accuracy in order to make it viable to switch off convection parametrization. This review describes the current state-of-the-art in operational CPM-based numerical weather prediction (NWP), primarily within the UK, and the historical development of CPMs. The characteristics of CPM systems and forecasts are highlighted and placed in an international context to recognize similar trends and highlight some differences. It is shown that the realism of CPM-based forecasts can provide improved subjective guidance on convection, and, when measured on appropriate scales, can improve rainfall forecasting skill compared to coarser-resolution NWP. Data assimilation techniques used with operational CPMs are reviewed and given historical context. Examples of new types of observations that may increase the skill of forecasts from improved initial conditions are discussed. CPM-based nowcasting systems are shown to provide considerable improvements in short-range forecasts of rapidly developing, intense systems. As a result, these CPM-based systems provide a new forecasting capability. Finally, the development of CPMs has also required new techniques to verify forecasts and define their skill. These have revealed that the lack of predictability of the smallest scales involving convection means that ensemble techniques are required to represent forecast uncertainty, resulting in a new capability to provide objective forecast probabilities of local precipitation.

Convection-permitting models: a step-change in rainfall forecasting

An ensemble Kalman filter for convective-scale data assimilation (KENDA) has been developed for the COnsortium for Small-scale MOdelling (COSMO) model. The KENDA system comprises a local ensemble transform Kalman filter (LETKF) and a deterministic analysis based on the Kalman gain for the analysis ensemble mean. The KENDA software suite includes tools for adaptive localization, multiplicative covariance inflation, relaxation to prior perturbations and adaptive observation errors. In the version introduced here, conventional data (radiosonde, aircraft, wind profiler, surface station data) are assimilated. Latent heat nudging of radar precipitation has also been added to the KENDA system to be applied to the deterministic analysis only or additionally to all ensemble members. The performance of different system components is investigated in a quasi-operational setting using a basic cycling environment (BACY) for a period of six days with 24 h forecasts. For this period and an additional 28 day period, deterministic KENDA forecasts are compared with forecasts based on the observation nudging data assimilation scheme, which is currently operational at the German Weather Service (Deutscher Wetterdienst, DWD). For our experiments, lateral boundary conditions for the regional model are given by a global ensemble Kalman filter for the ICOsahedral Nonhydrostatic (ICON) model. The performance of the KENDA system proves overall to be superior to the forecast quality of the operational nudging scheme, in particular with regard to precipitation. Latent heat nudging improves precipitation forecasts in both systems and has slightly more benefit in combination with the LETKF than with observation nudging.

Kilometre-scale ensemble data assimilation for the COSMO model (KENDA)

Installed capacities of wind and solar power have grown rapidly over recent years, and the pool of literature on very short-term (minutes- to hours-ahead) wind and solar forecasting has grown in line with this. This paper reviews established and emerging approaches to provide an up-to-date view of the field. Knowledge transfer between wind and solar forecasting has benefited the field and is discussed, and new opportunities are identified, particularly regarding use of remote sensing technology. Forecasting methodologies and study design are compared and recommendations for high quality, reproducible results are presented. In particular, the choice of suitable benchmarks and use of sufficiently long datasets is highlighted. A case study of three distinct approaches to probabilistic wind power forecasting is presented using an open dataset. The case study provides an example of exemplary forecast evaluation, and open source code allows for its reproduction and use in future work.

A review of very short-term wind and solar power forecasting

Since 2012 the Met Office has been running a short-range convective-scale ensemble prediction system over the UK, known as MOGREPS-UK. In this paper we consider MOGREPS-UK in its past, present and future configurations. We describe the evolution of the system during its first few years as an operational model and explain the rationale behind its development. The operational configuration of MOGREPS-UK is evaluated using neighbourhood verification techniques which allow the comparison of ensemble and deterministic forecasts in a probabilistic sense. We compare the performance of MOGREPS-UK to that of the higher resolution UK deterministic convective-scale model, the UKV, and show that over a three-month long trial MOGREPS-UK performs better for all variables considered. Plans of future upgrade options of MOGREPS-UK that take advantage of the increased computing capacity at the Met Office are discussed. Three different options were compared: increasing the domain size (now implemented), decreasing the horizontal grid-spacing, and increasing the number of ensemble members. Objective verification results from month-long winter and summer trials show that all options have their benefits, with the most improvement seen with the increase in ensemble size, particularly for precipitation.

The Met Office convective-scale ensemble, MOGREPS-UK

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/asl.1130

Progress, challenges, and future steps in data assimilation for convection‐permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021

Hourly 4D-Var in the Met Office UKV operational forecast model

Forecasting Research Technical Report 585 Benefit of convective-scale data assimilation and observing systems in the UK Models

The Met Office limited area model (LAM) data assimilation (DA) system is based on an hourly 4D-Var method with lateral boundary conditions (LBCs) provided by the Met Office global model (GM). This set-up was operationally introduced in summer 2017. The work presented here improved the representation of large-scale dynamics in the LAM DA, which is now integrated on an extended domain and therefore needs to represent also larger scales than were present in 2017. To improve the representation of the large-scale motions in the convective-scale system we choose to take advantage of the better estimation of these scales from the host model. The method presented here is called large-scale blending (LSB), a spectral nudging solution which is applied to the Met Office LAM (UKV). Spectral nudging allows the LAM evolution to be constrained by a host model with a better representation of the larger scales while preserving the smaller scales predicted by the LAM. In this paper we investigate different options for the implementation of LSB such as cut-off wavelength and update frequency. The LAM forecast is typically improved for various variables over a forecast range from about T+10 to T+20 by the introduction of LSB. We also see a reduction in the internal gravity wave activity generated when new lateral boundary conditions are introduced to the LAM from the latest GM forecast. This research proves the benefits of a better representation of large-scale motions for LAM forecasts. This article is protected by copyright. All rights reserved. 

Large‐scale blending in an hourly 4D‐Var framework for a NWP model

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Gareth Dow

Author Tools

Chat about Author

Papers

Hourly 4D-Var in the Met Office UKV operational forecast model

Forecasting Research Technical Report 585 Benefit of convective-scale data assimilation and observing systems in the UK Models

Large‐scale blending in an hourly 4D‐Var framework for a NWP model