Rainfall Nowcast

# QPE (Quantitative Precipitation Estimates)

 Quantitative precipitation estimates (QPE) refers to the analysis of ground precipitation at locations or over a region of interest. SWIRLS provides several methods to compute QPE: Radar QPE Barnes analysis using rain gauge data Co-kriging analysis, with both rain gauge and radar data QPE is useful for generating rainfall distribution maps (also known as isohyet charts), which facilitates the general public and disaster mitigation agencies to appreciate the actual situation and estimate risks. QPE data may also be coupled with geophysical and hydrological models to predict and prevent landslides and floods.
 Radar QPE is a real-time calibration approach for obtaining rainfall rate through the Z-R relation Z=aR^b. The coefficients a and b can be either static based on climatological values, or dynamically calibrated.
Barnes analysis interpolates data at grids from discrete rain gauge data with Gaussian weighting based on distance between data and estimation point:
Parameters Description
B Barnes estimation (mm)
N number of gauge report
G i-th gauge report
w weight of i-th gauge
h distance between gauge and estimation point

 Co-kriging analysis is a geostatistical technique to derive an optimal estimate of rainfall using both rain gauge and radar reflectivity data. While computationally intensive, co-kriging provides a sophisticated approach to estimate rainfall amount taking into account the error characteristics of rain gauge and radar measurements and their spatial correlation.

# Quality Control on Observations

 The weather radars and raingauges are maintained with regular calibration to ensure high data quality for input to the nowcasting systems. Automatic raingauge data are not only fundamental in quantitative rainfall analysis but also used as the ground truth in warning operation and forecast validation. Quality control is necessary before the data can be used quantitatively due to systematic and random errors. Substantial random errors and unreasonably small or false zero values can hamper accuracy of the analysis of precipitation and effective monitoring of heavy rain. To address the issue, SWIRLS incorporates a real-time rainfall data quality-control scheme based on co-kriging analysis when blending radar and raingauge data to generate the quantitative precipitation estimate (QPE). As a basis of the quality-control scheme, the co-kriging rainfall analysis was shown through a verification exercise to be superior to those obtained by the Barnes analysis and ordinary kriging of raingauge data. An example is illustrated below. Details of the algorithm are described in the paper Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis An example of co-kriging rainfall analysis and raingauge data quality-control valid at 09:05 h on 7 June 2008 (top), along with the rainfall isohyet map based on the Barnes analysis method with only raingauge data (middle). A radar image at 09:00h (bottom) revealed significant rainfall in Victoria Harbour.

# QPF (Quantitative Precipitation Forecasts)

 Quantitative precipitation forecast (QPF) is the prediction of the amount of rainfall that a particular area is expected to receive. SWIRLS currently uses the advection-based model where QPF is calculated through the following steps: Compare two successive radar images and analyze the motion field with variational optical flow; Predict the evolution of radar echoes with semi-Lagrangian advection (SLA) of radar echoes; and Convert forecast radar reflectivity to rainfall intensity with static / dynamically calibrated Z-R relationship. Following calculation of forecast rainfall amount at grids, the predicted rainfall amounts at designated locations are interpolated. Products such as forecast rainfall map and time series of rainfall amount at selected location are then generated for forecasters' reference or dissemination to users.
 "Real-time Optical flow by Variational methods for Echoes of Radar (ROVER)" is a real-time variational optical flow scheme, which adopts 1) a pre-processing step to enhance radar reflectivity images; and 2) a real-time variational optical flow technique. 1.   Pre-processing step In ROVER, the reflectivity fields are enhanced by transforming the gridded value with the following function: where Zc and ξ control the point of inflection and its sharpness. Figure 1. Original (left) and Enhanced (right) reflectivity map 2.   Real-time variational optical flow technique To generate the motion field, SWIRLS adopts the open source codes titled "VarFlow" (Harmat, 2014), developed based on the algorithm proposed in Bruhn et al. (2003). Figure 2. Motion field with arrows indicating motion vectors