The Heidke skill score (HSS) is used to quantitatively evaluate the simulations with different schemes:
HHS = 2(𝑎𝑑 − 𝑏𝑐)/ (𝑎 + 𝑐)(𝑐 + 𝑑) + (𝑎 + 𝑏)(𝑏 + 𝑑) (9)
294 where the four elements a-d for HSS, representing the numbers of “hits”, “false alarms”, “misses” and “correct negatives”, respectively, are calculated from a contingency table
(Table 1). HHS can not only judge well-simulated events (both hits and correct negatives, element a and d) but also account for erroneous forecast (b and c) (Barnston, 1992). A higher HSS (0 ~ 1) represents better skill. As shown in Table 1, pt is the threshold value and is set to be 2 mm covering most of the observed and simulated precipitation area, ps and po are the values from simulations and observations,
respectively.
青藏高原混合相积云中液相云微物理过程的影响
https://www.atmos-chem-phys-discuss.net/acp-2019-1063/
在数值模拟中经常发现对青藏高原降水的过度预测，这被认为与粗网格尺寸或不正确的大尺度强迫有关。除了证实模型网格大小的重要作用外，这项研究还表明，液相降水参数化是另一个关键原因，并且揭示了潜在的物理机制。
使用天气研究和预报（WRF）模型模拟典型的夏季高原降水事件，方法是将液相微物理过程的不同参数化引入常用的Morrison方案中，包括自动转换，吸积和夹带混合机制。所有模拟都可以再现降水的总体空间分布和时间变化。与低分辨率域相比，高分辨率域中的降水被低估了。在模拟降水过程中，吸积过程比其他液相过程更重要。采用考虑雨滴大小的吸积参数化方法，可使总表面降水最接近观测值，这是由海德克技能得分支持的。
