Exploiting new observations and data assimilation techniques for improved forecasting of convective precipitation

利用新的观测和数据同化技术改进对流降水的预报

基本信息

  • 批准号:
    NE/K008919/1
  • 负责人:
  • 金额:
    $ 9.48万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2013
  • 资助国家:
    英国
  • 起止时间:
    2013 至 无数据
  • 项目状态:
    已结题

项目摘要

Brief periods of intense rainfall can lead to flash flooding with the potential to cause millions of pounds of damage to property, and to threaten lives. Accurate flood warnings even just a few hours ahead can allow preparations to be made to minimize damage. In order to improve the prediction of these events, more accurate forecasts of heavy rainfall are needed, which can then be used to inform flood prediction and warning systems. The UK Met Office is developing a new numerical weather prediction system with the goal of improving severe weather forecasts. This is a computer model that solves mathematical equations representing atmospheric motions and other physical processes such as cloud formation, with a horizontal grid spacing of 1.5km. This allows a more accurate representation of fine-scale features and explicit representation of storms, but the results are still dependent on the accuracy of the starting conditions or initial data describing the current state of atmospheric variables such as winds, pressure, temperature and humidity. Initial conditions are usually estimated using a sophisticated mathematical technique known as data assimilation that blends observations with model information, taking account of the uncertainties in the data. In this project, we propose fundamental research to reduce initial condition errors. The work will be carried out in a partnership between the Universities of Reading, Surrey and the Met Office. We plan to investigate ways of extracting the maximum information from weather radar observations of precipitation and moisture in the lower parts of the atmosphere. Although rainfall is usually well observed by weather radar, severe precipitation can cause the radar beam to lose energy, and thus the weaker returned signal may be misinterpreted, giving a lower rain-rate than in reality. We will develop algorithms to correct for this and other problems caused by severe rainfall. Recently, we have also developed techniques to infer humidity information about the lower atmosphere, and we plan to optimize the method and investigate the observation error characteristics, to prepare for this data to be assimilated by the Met Office. One of our goals is to use observations to provide information on the small scales without degrading the large scale weather patterns, which are themselves likely to be accurate. However, currently much of the small scale observational information is being lost by ignoring correlations between observation errors. We will develop a generic approach for treating observation correlations for a range of observation types. We will investigate mathematical methods that both capture the maximum amount of information contained in the observations, while still being practical for operational computations, which have to take place within a limited time frame. Another goal is to develop innovative ways of treating moist processes that are largely absent from present-day assimilation systems. We plan to design and test efficient and effective ways of assimilating moisture information that respect the intricate dynamical and physical relationships that operate in the atmosphere. If successful, such new approaches will allow better use of cloud and rain affected observations than at present.Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Further studies will be made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations.
短时间的强降雨可能导致山洪暴发,有可能造成数百万英镑的财产损失,并威胁生命。准确的洪水警报,即使只是提前几个小时,也可以让人们做好准备,以尽量减少损失。为了改善对这些事件的预测,需要更准确的暴雨预报,然后可以用来通知洪水预报和预警系统。英国气象局正在开发一种新的数值天气预报系统,目的是改善恶劣天气预报。这是一个计算机模型,求解代表大气运动和其他物理过程(如云的形成)的数学方程,水平网格间距为1.5公里。这使得更准确地表示精细尺度特征和明确的风暴表示,但结果仍然取决于初始条件或描述风,压力,温度和湿度等大气变量当前状态的初始数据的准确性。初始条件通常使用称为数据同化的复杂数学技术进行估计,该技术将观测与模型信息相结合,并考虑到数据中的不确定性。在这个项目中,我们提出了基础研究,以减少初始条件的错误。这项工作将由萨里郡的阅读大学和英国气象局合作进行。我们计划研究如何从天气雷达观测大气低层的降水和湿度中提取最大信息。虽然天气雷达通常可以很好地观测到降雨,但严重的降水会导致雷达波束损失能量,因此较弱的返回信号可能会被误解,从而产生比实际更低的降雨率。我们将开发算法来纠正这个问题和其他由强降雨引起的问题。最近,我们还开发了推断低层大气湿度信息的技术,我们计划优化方法并调查观测误差特性,为气象局同化这些数据做准备。 我们的目标之一是利用观测提供小尺度的信息,而不降低大尺度天气模式的准确性。然而,目前许多小尺度的观测信息被忽略的观测误差之间的相关性丢失。我们将开发一种通用的方法来处理一系列观测类型的观测相关性。我们将研究数学方法,既能捕获观测中包含的最大信息量,又能在有限的时间范围内进行实用的操作计算。另一个目标是开发创新的方法来处理湿过程,这在当今的同化系统中基本上是不存在的。我们计划设计和测试吸收水分信息的高效和有效的方法,这些方法尊重在大气中运行的复杂的动力学和物理关系。如果成功的话,这种新方法将允许更好地利用云和雨影响的观测比目前更难预测的事实,一些事件本质上是不可预测的,即使有良好的数据同化和模式,由于他们的高敏感性,即使是在初始条件的小误差。将利用从模型和观测中得出的诊断结果,开展进一步研究,探讨这类事件可预测性低的动力学原因。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project
  • DOI:
    10.3390/atmos10030125
  • 发表时间:
    2019-03
  • 期刊:
  • 影响因子:
    2.9
  • 作者:
    S. Dance;S. Ballard;R. Bannister;P. Clark;H. Cloke;T. Darlington;D. Flack;S. Gray;L. Hawkness‐Smit
  • 通讯作者:
    S. Dance;S. Ballard;R. Bannister;P. Clark;H. Cloke;T. Darlington;D. Flack;S. Gray;L. Hawkness‐Smit
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Ian Roulstone其他文献

Ian Roulstone的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Ian Roulstone', 18)}}的其他基金

Inference in Complex Stochastic Dynamic Environmental Models
复杂随机动态环境模型中的推理
  • 批准号:
    EP/C006208/1
  • 财政年份:
    2006
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Research Grant

相似国自然基金

脊髓新鉴定SNAPR神经元相关环路介导SCS电刺激抑制恶性瘙痒
  • 批准号:
    82371478
  • 批准年份:
    2023
  • 资助金额:
    48.00 万元
  • 项目类别:
    面上项目
tau轻子衰变与新物理模型唯象研究
  • 批准号:
    11005033
  • 批准年份:
    2010
  • 资助金额:
    18.0 万元
  • 项目类别:
    青年科学基金项目
HIV gp41的NHR区新靶点的确证及高效干预
  • 批准号:
    81072676
  • 批准年份:
    2010
  • 资助金额:
    33.0 万元
  • 项目类别:
    面上项目
强子对撞机上新物理信号的多轻子末态研究
  • 批准号:
    10675110
  • 批准年份:
    2006
  • 资助金额:
    36.0 万元
  • 项目类别:
    面上项目

相似海外基金

Toward measures and behavioral trials for effective online AUD recovery support
采取措施和行为试验以提供有效的在线澳元复苏支持
  • 批准号:
    10643056
  • 财政年份:
    2023
  • 资助金额:
    $ 9.48万
  • 项目类别:
RUI: Predictive models with Incomplete and Fragmented Observations, and New Advances in Virtual Re-sampling for Big Data
RUI:具有不完整和碎片观测的预测模型,以及大数据虚拟重采样的新进展
  • 批准号:
    2310504
  • 财政年份:
    2023
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Standard Grant
The Manhattan HIV Brain Bank
曼哈顿艾滋病脑库
  • 批准号:
    10818199
  • 财政年份:
    2023
  • 资助金额:
    $ 9.48万
  • 项目类别:
The Oleander Project: High-resolution observations of the dynamic ocean between New Jersey and Bermuda
夹竹桃项目:新泽西州和百慕大之间动态海洋的高分辨率观测
  • 批准号:
    2241601
  • 财政年份:
    2023
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Continuing Grant
Particle Acceleration Region in Solar Flares Revealed by New-Generation Multi-Wavelength Observations
新一代多波长观测揭示太阳耀斑中的粒子加速区域
  • 批准号:
    23K03455
  • 财政年份:
    2023
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
New high-resolution observations of ocean surface current and winds from innovative airborne and satellite measurements
通过创新的机载和卫星测量对海洋表面流和风进行新的高分辨率观测
  • 批准号:
    2901696
  • 财政年份:
    2023
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Studentship
Collaborative Research: Towards a new framework for interpreting mantle deformation: integrating theory, experiments, and observations spanning seismic to convective timescales
合作研究:建立解释地幔变形的新框架:整合从地震到对流时间尺度的理论、实验和观测
  • 批准号:
    2311897
  • 财政年份:
    2022
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Continuing Grant
Infrared-Radio-follow-up Observations for Detection of the Magnetic Radio Emission of Extra Solar Planets: A New Window to Detect Exoplanets and Exomoons
探测系外行星磁射电发射的红外无线电跟踪观测:探测系外行星和系外卫星的新窗口
  • 批准号:
    2138122
  • 财政年份:
    2022
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Fellowship Award
Collaborative Research: Towards a new framework for interpreting mantle deformation: integrating theory, experiments, and observations spanning seismic to convective timescales
合作研究:建立解释地幔变形的新框架:整合从地震到对流时间尺度的理论、实验和观测
  • 批准号:
    2218568
  • 财政年份:
    2022
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Continuing Grant
Collaborative Research: Towards a new framework for interpreting mantle deformation: integrating theory, experiments, and observations spanning seismic to convective timescales
合作研究:建立解释地幔变形的新框架:整合从地震到对流时间尺度的理论、实验和观测
  • 批准号:
    2218695
  • 财政年份:
    2022
  • 资助金额:
    $ 9.48万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了