我如何处理不切实际的相对湿度计算使用蒸汽压力和饱和蒸汽压力?- 江南体育网页版- - - - -地球科学堆江南电子竞技平台栈交换 最近30从www.hoelymoley.com 2023 - 07 - 07 - t20:57:08z //www.hoelymoley.com/feeds/question/22828 https://creativecommons.org/licenses/by-sa/4.0/rdf //www.hoelymoley.com/q/22828 4 我如何处理不切实际的相对湿度计算使用蒸汽压力和饱和蒸汽压力? matlabcat //www.hoelymoley.com/users/24395 2021 - 09 - 15 - t19:47:38z 2021 - 09 - 21 - t14:21:23z < p >我使用这个广泛接受方程计算RH (%): < / p > < p > <跨类= " math-container " > $ RH = e \ * \压裂{100}{es (T)} $ < / span > < / p > < p >, < span class = " math-container " > e < / span >美元是蒸汽压力和<跨类= " math-container " > es (T) < / span >美元的饱和蒸汽压力温度<跨类= " math-container " > T < / span >美元。< / p > < p >我观察类< span = " math-container " > e < / span >美元(从0 <跨类= " math-container " > - < / span > 30美元hPa)。I calculate $es$ in R using one of the following equations (depending on the wet bulb temperature $Tw$ in relation to zero (to account for vapour pressure over liquid or solid water)):

f.es1 <- function(T) 6.107 * exp(17.38 * T/(239. + T)) # Tw >= 0 f.es2 <- function(T) 6.107 * exp(22.44 * T/(272.4 + T)) # Tw < 0

I have noticed that $RH$ (using the first equation above) is extremely large in some cases (e.g., 4352.567). This is occurring when $e$ is 0.6 and $es$ is 0.01378497.

I know I can scale the RH data to between 1:100, but I'm wondering if there is a better way of dealing with this? It is happening when $e$ and $es$ are extremely small. I'm guessing this is over really dry areas perhaps? Is there a better way of calculating $RH$ for these places?

This is some information about the dataset:

1 variables (excluding dimension variables): short vap[lon,lat,time] (Chunking: [2160,30,1]) standard_name: vapor_pressure long_name: vapor_pressure units: kPa add_offset: 0 scale_factor: 0.01 _FillValue: -32768 missing_value: -32768 description: Vapor Pressure dimensions: lon lat time coordinate_system: WGS84,EPSG:4326 3 dimensions: time Size:12 *** is unlimited *** standard_name: time long_name: time units: days since 1900-01-01 00:00:00 calendar: gregorian axis: T lon Size:2160 standard_name: longitude long_name: longitude units: degrees_east axis: X lat Size:1080 standard_name: latitude long_name: latitude units: degrees_north axis: Y 5 global attributes: CDI: Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi) Conventions: CF-1.6 history: Wed Sep 15 14:21:54 2021: cdo remapcon,r2160x1080 TerraClimate19812010_vap.nc WCvap_terraclimate_1981_2010.nc method: These layers from TerraClimate were creating using climatically aided interpolation of monthly anomalies from the CRU Ts4.0 and Japanese 55-year Reanalysis (JRA-55) datasets with WorldClim v2.0 climatologies. CDO: Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)

Headings in the picture below:

  • ind_e = e

  • ind_T_1500 = Temperature (at 3pm)

  • ind_es = Saturated vapour pressure

  • ind_Rh1500 = Relative humidity calculated for 3pm.

  • ind_Tx = Tmax

  • ind_Tn = Tmin

enter image description here

This is the updated dataframe (below). The e is slightly lower generally, but it still seems incorrect.

This is the updated dataframe
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