Improvement of stratocumulus representation in models by the use of high resolution observations

层积云在英格兰南部很常见，约有25%的时间出现。云层很薄，上面是晴朗的天空，所以如果它们散开，那么阴天就会突然变成晴朗的晴天;或者它们可以迅速发展并在天空中蔓延，将晴天变成阴沉的阴天。它们的形成、持久性和扩散令人惊讶地难以预测。在全球范围内，它们在寒冷的海水上形成广泛的云层，例如，在加州，秘鲁和纳米比亚的海岸。这些云层对地球的气候有着重要的影响，因为它们将阳光直接反射回太空，因此它们的存在具有整体的冷却效果。如果我们要对未来的全球变暖做出准确的预测，那么正确地表示这些云层未来范围的任何变化是很重要的。用于天气预报和预测未来气候变化的模型将大气划分为大的网格框，这些网格框通常深达500米，宽达数十或数百公里，每个框中只有两个数字用于描述云（例如云的数量和云水的质量）。层积云很难预测，因为它们通常只有100米深，可以遮挡太阳，但深度不足以填满模型网格框。在接近地球表面约1公里深的一层中，空气在垂直方向上不断地混合和搅拌，层积云就形成在这个混合的“边界”层的顶部。云的存在取决于地面或海洋表面的水分与边界层上方干燥空气的混合之间的微妙平衡，这些水分为云提供了养分，而边界层上方干燥空气的混合则倾向于分散它们。另一个重要的机制是在云中形成毛毛雨，这往往会去除水分，从而驱散云层。云滴本身是在小的尘埃颗粒上形成的，因此云的性质取决于空气中的尘埃或污染程度。这项建议的目的是在几年的时间里用地面上的激光雷达和雷达对层积云的垂直结构进行详细的观测。雷达发出无线电波的短脉冲;云将这些波中的一些散射回雷达，通过计算回波返回所需的时间和测量其强度，我们可以计算出云的高度和含水量，并确定云是否产生毛毛雨。云和毛毛雨滴的垂直速度可以从“多普勒频移”（反射波的无线电频率的变化）推断出来。激光雷达的工作原理相同，但使用的是光;我们有激光雷达，它可以感知空气中尘埃颗粒反射的光，这样我们就可以看到有多少这些颗粒存在，然后从激光雷达回波的多普勒频移中感知空气的垂直运动。其他激光雷达可以检测空气中有多少水分，因此通过结合这些观测结果，我们可以测量水分进入云层的垂直运动，从云层中落下的毛毛雨，云顶干燥空气的混合，并了解这些与云的演变及其持续性或破裂的关系。一旦我们理解了这些过程，我们就可以尝试改进预测。为此，我们正在与气象局和欧洲中期天气预报中心合作，以便我们可以测试在其业务预报模型中表示这些层积云的改进方法。其目的是制作模型，提供更好的天气预报，一天是多云还是晴天。此外，更好地表示寒冷海洋上空广泛的层积云区域将增加我们对全球警报预测准确性的信心。

Stratocumulus clouds are common over S England occurring about 25% of the time. The clouds are rather thin with clear sky above, so if they break up then an overcast day is suddenly transformed into a clear sunny day; alternatively they can rapidly develop and spread over the sky changing a sunny day into a gloomy overcast one. Their formation, persistence and dispersion are surprisingly difficult to forecast. On a global scale they form widespread cloud sheets over the cold ocean water, for example, off the coast of California, Peru and Namibia. These cloud sheets have an important effect on the climate of the earth, as they reflect the sunlight straight back to space rather, and so their presence has an overall cooling effect. It is important to represent any changes in the future extent of these clouds sheets correctly if we are to have accurate predictions of future global warming. The models used for weather forecasting and for predicting future climate change split the atmosphere into large grid boxes which are typically up to 500m deep and tens or hundreds of km across, with only two numbers used to describe the cloud in each box (e.g the amount of cloud and the mass of cloud water). Stratocumulus clouds are difficult to forecast because they are often only 100m deep and can block out the sun, but are not deep enough to fill a model grid box. In a layer about 1km deep close to the surface of the earth the air is being continuously mixed and stirred in the vertical and the stratocumulus clouds form at the top of this mixed 'boundary' layer. The existence of the clouds is governed by a delicate balance between the moisture from the surface of the ground or the ocean which feeds the clouds, and the mixing of dry air above the boundary layer tending to disperse them. Another important mechanism is the formation of drizzle in the clouds which tends to remove the moisture and so disperse the clouds. The cloud droplets themselves are formed on small dust particles, so the properties of the clouds are dependent upon the level of dust or pollution in the air. The purpose of this proposal is to make detailed observations of the vertical structure of stratocumulus clouds over a period of several years with lidars and radars on the ground. A radar sends out short pulses of radio waves; the cloud scatters some of these waves back to the radar, and by timing how long the echo takes to be returned and by measuring its strength we can calculate the height of the cloud and how much water it contains and identify if the cloud is producing drizzle. The vertical velocity of the cloud and drizzle drops can be inferred from 'Doppler shift', the change in radio frequency of the reflected wave. A lidar works on the same principle but uses light; we have lidars that sense the light reflected of dust particles in the air so we see how many of these particles are present, and then sense the vertical movement of the air from the Doppler shift of the lidar echoes. Other lidars can detect how much moisture is in the air and so by combining these observations we can measure the vertical movement of moisture into the clouds, the drizzle falling out of the clouds, the mixing of dry air at cloud top and see how these relate to the evolution of the cloud and its persistence or break up. Once we understand these processes we can try to improve the forecasts. To this end we are collaborating with the Meteorological Office and the European Centre for Medium Range Weather Forecasting, so we can test out improved means of representing these stratocumulus clouds in their operational forecast models. The aim is to produce models which provide better weather forecasts of whether a day is to be cloudy or sunny. In addition, a better representation of the extensive regions of stratocumulus over the cold oceans will increase our confidence in the accuracy of the predictions of global warnings.