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The letters in the top row stand for months: January, February, etc. The bars and numbers convey the following information: The blue bars represent the average amount of precipitation (rain, snow etc.) that falls in each month. The blue numbers are the amount of precipitation in either millimeters (liters per square meter) or inches.
Average monthly precipitation (in mm) for selected cities in Asia ; City Country Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ref. Mawsynram: India: 133.0 8.3 15.7 27.4 29.8 26.0 5.7
= 4.1 R −0.21 mm −1 (equivalent to 41 R −0.21 cm −1 in the reference [4]), R being the rainrate in stratiform precipitation in millimeters per hour; D = raindrop diameter in mm The units of N 0 are sometimes simplified to cm −4 but this removes the information that this value is calculated per cubic meter of air.
Given the coarse accuracy of the Blaney–Criddle equation, it is recommended that it be used to calculate evapotranspiration for periods of one month or greater. [ 1 ] The equation calculates evapotranspiration for a 'reference crop', which is taken as actively growing green grass of 8–15 cm height.
The rainfall data can be either a user-defined time series or come from an external file. Several different popular rainfall file formats currently in use are supported, as well as a standard user-defined format. The principal input properties of rain gages include: rainfall data type (e.g., intensity, volume, or cumulative volume)
Most modern rain gauges generally measure the precipitation in millimetres in height collected during a certain period, equivalent to litres per square metre. Previously rain was recorded as inches or points, where one point is equal to 0.254 mm or 0.01 of an inch. [16] Rain gauge amounts are read either manually or by automatic weather station ...
It is important to note that the curve number methodology is an event-based calculation, and should not be used for a single annual rainfall value, as this will incorrectly miss the effects of antecedent moisture and the necessity of an initial abstraction threshold.
Rainfall forecasts can be verified a number of ways. Rain gauge observations can be gridded into areal averages, which are then compared to the grids for the forecast models. Weather radar estimates can be used outright, or corrected for rain gauge observations. [4] Several statistical scores can be based on the observed and forecast fields.