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A new technique for measuring global rainfall

测量全球降雨量的新技术

基本信息

批准号：
NE/T001216/1
负责人：
Anthony Illingworth
金额：
$ 68.3万
依托单位：
University of Reading
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FT001216%2F1
关键词：
new technique measuring global rainfall

项目摘要

Precipitation is a vital element for life on Earth. Agriculture and the food supply depend upon the global distribution of precipitation, so a knowledge of when, where and how much rain falls is of paramount importance to society, but excessive amounts lead to flooding, loss of life and damage to property. We need to improve weather forecast models so they can better predict when and where heavy rain is likely to cause flash floods and any mitigating actions can be focused on areas at risk. We also need better confidence in the ability of climate models to predict changes in global rainfall patterns so that long term policy decisions are better informed.Global climate and weather forecasting models have a resolution of several km and each model 'grid-box' (size 1km or greater) can have just two or three variables expressing the properties of the clouds in the grid box. The individual collisions between cloud particles to produce precipitation cannot be modelled, but instead the rate of conversion of cloud water into precipitation is approximated or 'parameterised' in terms of the large-scale variables such as the mass of cloud water per cubic metre within the grid box. We know that these parameterisation schemes are imperfect, and need global observations of rainfall to check how well these models capture the statistical properties of the rainfall in the present climate so we can identify when and where the schemes are failing and how they could be improved.It is surprisingly difficult to measure global rainfall. Rain gauges have been in use for hundreds of years, but they are only a point measurement and are restricted to land. CloudSat, launched in 2006, provides the best estimates of global rainfall and these data have been used for model validation. The technique relies on the fall of the radar signal from the ocean surface caused by the attenuating rain, the so-called PIA ('path integrated attenuation') method, but direct validation is difficult.We propose implementation of a new 'Gradient' technique that derives the rain rate directly from the gradient of the radar reflectivity profile that results from the attenuating rain and has two unique advantages: a) the error in the rain rate can be estimated from the goodness of fit of the profile to a straight line, and b) exactly the same algorithm has been used by 94GHz radars on the ground where it has been validated by co-located rapid response rain gauges. Initial tests show that the rainfall derived from the new 'Gradient' method is significantly greater than values from the PIA technique, so the first task is to reconcile these differences by analyzing the assumptions made by the PIA method. Next, we will refine 'Gradient' method and its errors by analysing and validating more CloudSat and ground-based 94GHz rainfall observations.A new global rainfall data set with quantified errors will be made available to the science community. In collaboration with climate and weather forecast modellers, the observed geographical and seasonal variations in rainfall statistics will be compared with their representation in the models to identify when and where the parameterization schemes have shortcomings. To predict any future global warming we need to understand the current balance of incoming solar and outgoing thermal infra-red radiation; this current balance is also sensitive to any changes in the energy transported by the mean global precipitation that should be revealed by the new CloudSat estimates. The 94GHz radar on the EarthCARE satellite (launch 2022) has an additional Doppler capability. We will use the ground based 94GHz Doppler data to establish if the Doppler on EarthCARE can provide improved rain rate estimates. We will also examine how future scanning 94GHz radars could provide a larger sample of rainfall; potentially such data in near real-time could be assimilated in near real-time to further improve forecasts of heavy rainfall.

降水是地球生命的重要因素。农业和粮食供应取决于降水的全球分布，因此了解何时、何地和降雨量福尔斯对社会至关重要，但降雨量过多会导致洪水、生命损失和财产损失。我们需要改进天气预报模型，以便更好地预测大雨可能在何时何地引发山洪暴发，并将任何缓解行动集中在风险地区。我们还需要对气候模型预测全球降雨模式变化的能力有更好的信心，以便更好地了解长期政策决策。全球气候和天气预报模型的分辨率为几公里，每个模型“网格盒”（大小为1公里或更大）可以只有两个或三个变量来表达网格盒中云的属性。云粒子之间产生降水的单个碰撞无法模拟，但云水转化为降水的速率是近似的或“参数化”的大尺度变量，如网格框内每立方米云水的质量。我们知道这些参数化方案是不完善的，需要对全球降雨量进行观测，以检查这些模型在多大程度上捕捉到了当前气候下降雨量的统计特性，这样我们就可以确定这些方案在何时何地失败，以及如何改进。雨量计已经使用了数百年，但它们只是一个点测量，并且仅限于陆地。2006年发射的CloudSat提供了全球降雨量的最佳估计，这些数据已用于模型验证。该技术依赖于雷达信号从海洋表面的衰减雨，所谓的PIA造成的下降（“路径综合衰减”）方法，但直接验证是困难的。我们提出了一种新的“梯度”技术，直接从雷达反射率剖面的梯度得出降雨率，该雷达反射率剖面是由衰减的降雨造成的，具有两个独特的优点：a）降雨率的误差可以从廓线与直线的拟合优度来估计，以及B）地面上的94 GHz雷达已经使用了完全相同的算法，其中该算法已经被共同定位的快速响应雨量器验证。初步测试表明，新的“梯度”方法得出的降雨量显着大于从PIA技术的值，所以第一个任务是通过分析PIA方法所作的假设来调和这些差异。接下来，我们将通过分析和验证更多的CloudSat和地面94 GHz降雨观测数据来改进“梯度”方法及其误差。一个新的具有量化误差的全球降雨数据集将提供给科学界。将与气候和天气预报建模人员合作，将观测到的降雨量统计中的地理和季节变化与其在模型中的表现进行比较，以确定参数化方案何时何地存在缺陷。为了预测未来的全球变暖，我们需要了解当前入射太阳能和输出热红外辐射的平衡;这种平衡对全球平均降水量传输的能量的任何变化都很敏感，这应该由新的CloudSat估计来揭示。EarthCARE卫星（2022年发射）上的94 GHz雷达具有额外的多普勒能力。我们将使用基于地面的94 GHz多普勒数据，以确定是否在EarthCARE多普勒可以提供改进的降雨率估计。我们还将研究未来的94 GHz扫描雷达如何提供更大的降雨样本;近实时的此类数据可能会被近实时同化，以进一步改善强降雨的预报。