DOI: 10.5061/dryad.8kprr4xnq
title: High-resolution CONUS-wide downscaled rainfall estimates (HRCDRE)

description: The spatiotemporal character of rainfall is particularly important for
 hydrologic modeling, as well as hydroclimatic risk estimation and impact
 assessment. Existing atmospheric reanalysis datasets offer extensive
 record lengths and global coverage, but usually their spatial resolution
 is coarse for distributed hydrologic simulations at small spatial scales.
 On the other hand, the temporal coverage of high-resolution radar-based
 rainfall estimates can be rather short for risk applications. To address
 these shortcomings, we simultaneously bias-correct and downscale a
 state-of-the-art atmospheric reanalysis (ERA5) rainfall dataset, using the
 radar-based Stage IV precipitation product as fine resolution reference,
 to develop an hourly CONUS-wide precipitation product over a 4-km grid,
 which extends back to 1979. In this regard, we refine an existing
 parametric quantile mapping framework based on a two-component theoretical
 distribution model, where we impose continuity of the parametric forms via
 optimal threshold selection to transition between higher and lower rain
 rates. An evaluation over the probability frequency and time domains,
 using NOAA’s raingauge measurements as benchmark, reveals that the
 developed product benefits from the strengths of the calibration datasets,
 demonstrating good performance and robust behavior over all studied time
 periods and Köppen climate classification zones, including snow-prone
 regions or areas where mesoscale convective systems become dominant. The
 accuracy of the yielded high spatial-resolution rain rates, especially in
 low probability events, shows that the developed product can be
 effectively used for hydroclimatic risk applications and frequency
 analysis, while its high temporal and spatial resolution makes it
 particularly useful for distributed hydrologic modeling.
description: We refined an existing
 parametric quantile-quantile (Q-Q) mapping framework based on a
 two-component theoretical distribution model, where we impose continuity
 of the parametric forms through a selection of an optimal threshold to
 transition between higher and lower rain rates. The parametric Q-Q mapping
 nonlinear transformation is obtained by matching the theoretical quantiles
 of the incorporated rainfall fields at different spatial resolutions
 within a certain calibration period, after fitting proper theoretical
 distribution models to the available data. Higher rain rates are
 parameterized using a Generalized Pareto (GP) distribution model, fitted
 above a sufficiently high threshold. For the case of lower rainfall
 intensities, we initially conducted tests to assess the suitability of the
 exponential, gamma, and lognormal parametric forms in fitting
 the empirical rain rates below the selected threshold, using the method of
 Maximum Likelihood (ML). The lognormal model demonstrated better
 performance in reproducing rainfall intensities over the whole study
 domain and was, therefore, adopted. To ensure the best possible
 distribution fit, the threshold to transition between higher and lower
 rainfall intensities was selected by minimizing the Anderson-Darling
 statistic. For more details on the employed techniques the user is
 referred to the accompanying publication; see Emmanouil et al.
 (2021). 
description: A detailed description of the dataset is included in the
 accompanying readme.txt file. Some important points: 
 Software used for the creation of the
 dataset:  Data were created using
 Matlab 2019a (R2019a). 
 Methods for processing the data: 
 Any package and/or software that handles NetCDF files
 can be employed by the user to process the data. 
 For Matlab, the user is referred to the ncdisp() and ncread() functions. Variable List: <strong>precip</strong> Size: (8760xN) or (8784xN), where N denotes the number of data points contained in each ERA5 grid cell (see also Grid_cell_IDs.txt).  Units: Millimeters per hour (mm/h). Description: Set of hourly precipitation estimates, corresponding to a selected year and grid cell ID, with a spatial resolution of 4x4 km. <br>                                 Missing hourly estimates are included as NaN.   <strong> lat </strong>                                                                                         Size: (1xN), where N denotes the number of data points contained in each ERA5 grid cell (see also Grid_cell_IDs.txt).       Units: Degrees North (degN). Description: latitude of the downscaled ERA5 precipitation product's estimation points (geographic coordinate specifying the north–south position).<br>      <strong>lon </strong>                                                                                        Size : (1xN), where N denotes the number of data points contained in each ERA5 grid cell (see also Grid_cell_IDs.txt).  Units: Degrees East (degE). Description: Longitude of the downscaled ERA5 precipitation product's estimation points (geographic coordinate specifying the east–west position).<br>