SHARPEN: A Scheme to Restore the Distribution of Averaged Precipitation Fields

Abstract

A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN, which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

A New Event‐Based Error Decomposition Scheme for Satellite Precipitation Products

“…For seasonal analysis, the underestimation in summer is primarily attributed to the intensity underestimation ( Hit‐Negative ), whereas that in winter is predominantly due to the “ Miss‐ ” components (Figure 4c1). The former might be attributed to satellites' inherent under‐representativeness of intense precipitation prevalent in summer due to the “regression‐to‐mean” tendency inherent in the Bayesian inversion, coupled with the “smoothing effect” of IMERG's “morphing” interpolation algorithm (Rajagopal et al., 2021; Tan et al., 2021). In contrast, the latter is related to the large uncertainties over ice/snow covers in winter, where less accurate IR retrievals are used instead of PMW observations (Huffman et al., 2019a; Passive Microwave Algorithm Team Facility, 2017).…”

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

“…The fusion algorithm calculates a weighted average of the results obtained from infrared and motion vector propagation. During this process, although the central intensity of the extrapolation remains unchanged, if the position of the extrapolation does not align perfectly with the IR position, the weighted average can overestimate the precipitation coverage and underestimate the central intensity (Rajagopal et al., 2021; Tan et al., 2020). By ignoring the impact of the cloud motion vector morphing algorithm, the spatial distribution of precipitation in FY‐3E is consistent with that in IMERG, which partly shows that the retrieval algorithm can describe the relationship between brightness temperature and precipitation accuracy.…”

Tracking Mesoscale Convective Systems in IMERG and Regional Variability of Their Properties in the Tropics

“…The bias in total rainfall is slightly lower when both biases are combined. SHARPEN, a new averaging method for IMERG, will significantly reduce these issues in IMERG V07 (Tan et al., 2020). In Section 3.6, we discuss using a 1 mm hr −1 precipitation threshold to mitigate the problem of spurious rain areas.…”