A new technique for measuring global rainfall

降水是地球上生命的重要元素。农业和粮食供应取决于降雨量的全球分布，因此，了解何时、何地和降雨量对社会至关重要，但过多的降雨量会导致洪水、生命损失和财产损失。我们需要改进天气预报模型，以便它们能够更好地预测暴雨可能在何时何地导致山洪暴发，任何缓解行动都可以集中在有风险的地区。我们还需要对气候模型预测全球降雨模式变化的能力更有信心，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，每个模型的网格盒(大小为1公里或更大)可以只有两到三个变量来表示网格盒中的云的属性。云粒子之间产生降水的个别碰撞无法模拟，而是云水转化为降水的速率是根据大尺度变量(如网格箱内每立方米云水的质量)来近似或“参数化”的。我们知道这些参数化方案是不完美的，需要全球降雨观测来检查这些模型在当前气候下捕获降雨统计特性的程度，以便我们能够确定这些方案何时何地失败以及如何改进。测量全球降雨量令人惊讶地困难。雨量计已经使用了几百年，但它们只是一个点的测量，仅限于陆地。2006年推出的Cloudsat提供了对全球降雨量的最佳估计，这些数据已用于模型验证。该技术依赖于降雨衰减引起的雷达信号从海面上落下，即所谓的路径综合衰减(PIA)方法，但直接验证是困难的。我们提出了一种新的梯度技术，它直接从雷达反射率剖面的梯度中获得雨率，该技术具有两个独特的优点：a)雨率误差可以通过轮廓对直线的拟合程度来估计；b)94 GHz雷达已经在地面上使用了完全相同的算法，并通过共置的快速响应雨量计进行了验证。初步测试表明，新的“梯度”方法得出的降雨量明显大于PIA技术得出的降雨量，因此，第一个任务是通过分析PIA方法所做的假设来协调这些差异。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量数据集将提供给科学界。将与气候和天气预报模型师合作，将观测到的降雨统计数据的地理和季节变化与其在模式中的表现进行比较，以确定参数化方案何时何地存在缺陷。要预测未来的全球变暖，我们需要了解当前传入的太阳和传出的热红外辐射的平衡；这种当前的平衡也对新的云卫星估计应该揭示的全球平均降水量所传输的能量的任何变化很敏感。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94 GHz地面多普勒数据来确定EarthCARE上的多普勒是否可以提供更好的降雨率估计。我们还将研究未来扫描94 GHz雷达如何能够提供更大的降雨样本；潜在地，这种近乎实时的数据可以被近乎实时地同化，以进一步改进对暴雨的预报。

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.