A new technique for measuring global rainfall

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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.