RainNet: A Large-Scale Imagery Dataset for Spatial Precipitation Downscaling
Authors: Xuanhong Chen
Kairui Feng
Naiyuan Liu
Yifan Lu
Bingbing Ni
Ziang Liu
Maofeng Liu
Venue: ICLR 2022 Submitted (2022)
Keywords: Dateset
Downscaling
Computer Vision
Physics
Weather Forecast

Abstract: Contemporary deep learning frameworks have been applied to solve meteorological problems (\emph{e.g.}, front detection, synthetic radar generation, precipitation nowcasting, \emph{e.t.c.}) and have achieved highly promising results. Spatial precipitation downscaling is one of the most important meteorological problems. However, the lack of a well-organized and annotated large-scale dataset hinders the training and verification of more effective and advancing deep-learning models for precipitation downscaling. To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named \emph{RainNet}, which contains more than $62,400$ pairs of high-quality low/high-resolution precipitation maps for over $17$ years, ready to help the evolution of deep models in precipitation downscaling. Specifically, the precipitation maps carefully collected in RainNet cover various meteorological phenomena (\emph{e.g.}, hurricane, squall, \emph{e.t.c}.), which is of great help to improve the model generalization ability. In addition, the map pairs in RainNet are organized in the form of image sequences ($720$ maps per month or 1 map/hour), showing complex physical properties, \emph{e.g.}, temporal misalignment, temporal sparse, and fluid properties. Two machine-learning-oriented metrics are specifically introduced to evaluate or verify the comprehensive performance of the trained model, (\emph{e.g.}, prediction maps reconstruction accuracy). To illustrate the applications of RainNet, 14 state-of-the-art models, including deep models and traditional approaches, are evaluated. To fully explore potential downscaling solutions, we propose an implicit physical estimation framework to learn the above characteristics. Extensive experiments demonstrate that the value of RainNet in training and evaluating downscaling models.

Under review as a conference paper at ICLR 2022
A LARGE-SCALE IMAGERY DATASET FOR SPATIAL PRECIPITATION DOWNSCALING
RAINNET:
Anonymous authors
Paper under double-blind review
ABSTRACT
Contemporary deep learning frameworks have been applied to solve meteorolog- ical problems (e.g.. front detection, synthetic radar generation, precipitation now- casting, e.f.c.) and have achieved highly promising results. Spatial precipitation downscaling one of the most important meteorological problems. However, the lack of a wrll-organized and annotated large-scale dataset hinders the training and verification of more effective and advancing deep-learming models for precip- itation downscaling. To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named RainNer, which contains more than 62, 400 pairs of high-quality lowhhigh-resolution precipitation maps for over 17 years, ready to help the evolution of deep models in precipitation downscal- ing. Specifically, the precipitation maps carefully collected in RainNet cover var- ious meteorological phenomena (e.g., hurricane, squall, e.t.c.), which is of great help to improve the model generalization ability. In addition, the map pairs RainNet are organized in the form of image sequences (720 maps per month map/hour), showing complex physical properties, e.g., temporal misalignment, temporal sparse, and fluid properties. Two mechine-learning-offe specifically introduced to evaluate or verify the comprehensive performance of the trained model, (e.g., prediction maps reconstruction accuracy). To illustrate the applications of RainNet, 14 state-of-the-art models, including deep models and traditional approaches, lutions, we propose an implicit physical estimation framework to learn the above characteristics. Extensive experiments demonstrate that the value of RainNet in training and evaluating downscaling models.
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metrics are
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evaluated. To fully explore potential downscaling
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INTRODUCTION
25 Deep learning has made an enormous breakthrough in the field of computer vision, which is ex- tremely good at extracting valuable knowledge from numerous amounts of data. In recent years, 27 with computer science development, a deluge of Earth system data is continuously being obtained, coming from sensors all over the earth and even in space. These ever-increasing massive amounts of data with different sources and structures challenge the geoscience community. which lacks practi- cal approaches understand and further utilize the raw data (Reichstein al. (2019)). Specifically, several preliminary works (Groenke et al. (2020): White et al. (2019); He et al. (2016); Ravuri et al. 2021 Angell& Sheldon 2018): Veillette al. (2020)) try to introduce machine learning and deep leamning frameworks to solve meteorological problems, e.g., spatial precipitation downscaling.
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34 In this paper. we focus on the spatial precipitation downscaling task. Spatial precipitation down- scaling is a procedure to infer high-resolution meteorological information from low-resolution vari- ables, which one of the most important upstream components for meteorological task (Bauer al. 2015)). The precision of weather and climate prediction is highly dependent the resolution and reliabiiity of the initial environmental input variables, and spatial precipitation downscaling is the most promising solution. The improvement of the weather/climate forecast and Geo-data quality saves tremendous money and lives; with the fiscal year 2020 budget over $1 billion, NSF funds thousands of colleges in the U.S. to research on these topics (NSF (2020)).
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42 Unfortunately, there are looming issues hinders the research of spatial precipitation downscaling in the machine learning community: 1). Lack of "machine-learming ready" datasets. The existing

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To alleviate these obstacles. we propose the first large-scale spatial precipitation downscaling dataset named RainNet, which contains more than 62, 400 pairs of high-qualit itation maps for over 17 years, ready to help the evolution of deep models in spatial precipitation downscaling. The proposed dataset covers more than 9 million square kilometers of land area, which contains both wet and dry seasons and diverse meteorological phenomena. To facilitate DL/ML and other researchers to use RainNet, we introduce 6 most concerning indices to evaluate downscaling models: mesoscale peak precipitation error (MPPE), heavy rain region error (HRRE), cumulative precipitation mean square error (CPMSE), cluster mean distance (CMD), heavy rain transition speed (HRTS) and average miss moving degree (AMMD). In order to further simplify the application of in- dices, abstract them into two weighted and summed metrics: Precipitation Error Measure (PEM) 73 and Precipitation Dynamics Error Measure (PDEM). Unlike video super-resolution, the motion of the precipitation region is non-rigid (i.e., fluid), while video super-resolution mainly concerns rigid Under review as a conference paper at ICLR 2022
saachine-leaming-aee based downscaling methods are only applied to ideal retrospective problems and verified on simulated datasets (e.g., mapping bicubic of precipitation generated by weather fore- cast model to original data (Berrisford et al. (20IT)), which significantly weakens the credibility of the feasibility, practicabilityy and effectiveness of the methods. It is worth mentioning that the data obtained by the simulated degradation methods (e.g., bicubic) is completely different from the real data usually collected by two measurement systems (e.g., satellite and radar) with different precision. The lack of well-organized and annotated large-scale dataset hinders the training and verification of more effective and complex deep-learmin models for precipitation downscaling. 2).
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Lack of tailored metries to evaluate machine-learning based frameworks. Unlike deep learning (DL) and machine learning (ML) communities, scientists in meteorology usually employ maps/charts to assessing downscaling models case by case based on domain knowledge (He et al. (2016); Walton
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er al. 2020)), which hinders the application of Rainnet in /MM communities. For example, (He 55
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ct al. (2016)) use log-semivariance (spatial metrics for local precipitation), quantile-
maps to analyzing the maps. 3). an efficient downscaling deep-learning framework should be established. Contrary to image data, this real precipitation dataset covers various types of real meteorological phenomena (e.g.. Hurricane, Squall, e.t.c.), and shows the physical characters (e.g., temporal mis- alignment, temporal sparse and fluid properties, e.t.c.) that challenge the downscaling algorithms. Traditional computationally dense physics-driven downscaling methods are powerless to handle the increasing meteorological data size and flexible to multiple data sources.
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wwwhigh-resoluttin precip-
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75 body motion estimation. To fully explore how to alleviate the mentioned predicament, we propose
76 an implicit dynamics estimation driven downscaling deep learning model. Our model hierarchi-
77 cally aligns adjacent precipitation maps, that is, implicit motion estimation, which is very simple
78 but exhibits highly competitive performance. Based on meteorological science, we also proved that
79 the dataset we constructed contained the full information people may need to recover the higher resolution observations from lower resolution ones.
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The main contributions of this paper are:
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To the best of our knowledge, we present the first REAL (non-simulated) Large-Scale Spa- tial Precipitation Downscaling Dataset for deep learning:
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We introduce 2 simple metrics to evaluate the downscaling models;
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We propose downscaling model with strong competitiveness. We evaluate 14 competitive potential solutions on the proposed dataset, and analyze the feasibility and effectiveness of these solutions.
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BACKGROUND
89 At the beginning of the 1hth century, geoscientists recognized that predicting the state of the atmo- sphere could be treated as an initial value problem of mathematical physics, wherein future weather 90
91 is determined by integrating the governing partial differential equations, starting from the observed current weather. Today, this paradigm translates into solving system of nonlinear differential 93 equations at about half a billion points per time step and accounting for dynamic, thermodynamic, radiative, and chemical processes working on scales from hundreds of meters to thousands of kilo- 95 meters and from seconds to weeks (Bauer et al.] (2013). The Savier-Stokes and mass continuity
96 equations (including the effect of the Earth's rotation), together with the first law of thermodynamics
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Hurricane Florence
Humoone
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Squill
Sealll
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LR
Oct 26-28 2010
Figure 1: Dataset Visualization. Please zoom-in the figure for better observation. Please note that the details of the precipitation map are partially lost due file compression. Here plot 2 groups of typical meteorological phenomena (hurricane and squall) in the dataset. To learn more about the dataset, please visit our project website (coming soon) and supplementary material.
87 and the ideal gas law, represent the full set of prognostic equations in the atmosphere, describing the change in space and time of wind, pressure, density and temperature is described (formulas given in supplementary) (Bauer et al. (015)). These equations have to be solved numerically using spatial and temporal discretization because of the mathematical intractability of obtaining analytical solu- tions, and this approximation creates a distinction between so-called resolved and unresolved scales of motion.
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2.1
SPATIAL DOWNSCALING OF PRECIPITATION
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The global weather forecast model, treated as computational problem, relying on high-quality initial data input. The error of weather forecast would increase exponentially over time from this initial error of input dataset. Downscaling is of the most important approaches improve the initial input quality. Precipitation is one of the essential atmospheric variables that are related to daily life. It could easily be observed, by all means, e.g., gauge station, radar, and satellites. Applying downscaling methods to precipitation and creating high-resolution rainfall is far more meaningful than deriving other variables, while it is the most proper initial task to test deep learning's power in geo-science. The traditional downscaling methods be separated into dynamic and statistical downscaling.
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Dynamic downscaling treats the downscaling as optimization problem constraint the physical laws. The dynamic downscaling methods find the most likely precipitation over space and time under the pre-defined physical law. It usually takes over hours to downscale 6-hour precipitation scenario globally on supercomputers (Courtier et al. (1994). As the dynamic downscaling relying pre-defined known macroscopic physics, more flexible weather downscaling framework that
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could easily blend different sources of observations and show the ability to describe more complex physical phenomena on different scales is desperately in need.
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Statistical downscaling is trying to speed up the dynamic downscaling process. The imput of statisti- cal downscaling is usually dynamic model results or two different observation datasets on different scales. However, due to the quality of statistical downscaling results, people rarely apply statistical 122
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downscaling to weather forecasts. These methods are currently applied in the tasks not requir- ing high data quality but more qualitative understanding, e.g, climate projection, which forecasts the weather for hundreds of years on coarse grids and using statistical downscaling to get detailed knowledge of medium-scale future climate system.
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RAINNET: SPATIAL PRECIPITATION DOWNSCALING IMAGERY DATASET
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3.1
DATA COLLECTION AND PROCESSING
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To build up standard realistic (non-simulated) downscaling dataset for computer vision, we selected the eastern coast of the United States, which covers a large region (7 million km
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105 W, 25 50% N. GNU Free Documentation License 1.2) and has a 20-year high-quality
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precipitation observations. We collected two precipitation data sources from National Stage IV QPE
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Product (StageIV (Nelson et al. (2016); ); high resolution at 0.04 (approximately 4km), GNU Free Documentation License 1.2) and North American Land Data Assimilation System (NLDAS (Xia
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al. 2012)) ) low resolution at 0.125 (approximately 13km)). StageIV is mosaicked into na- tional product at National Centers for Environmental Prediction (NCEP), from the regional hourly/6- hourly multi-sensor (radar+gauges) precipitation analyses (MPEs) produced by the 12 River Fore- cast Centers over the continental United States with some manual quality control done the River spatially-and-Ha and-temporally con- sistent datasets from the gauges and remote sensors to support modeling activities. Both products are hourly updated and both available from 2002 to the current age.
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Forecast Centers (RFCs). NLDAS is constructed quality-controlles
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In dataset, further selected the eastern coast region for rain season (July November, covering hurricane season; hurricanes pour over 10% annual rainfall in less than 10 days). We matched the coordinate system the lat-lon system for both products and further labeled all the hurricane periods happening in the last 17 years. These heavy rain events are the largest challenge for weather forecasting and downscaling products. As heavy rain could stimulus a wide-spreading flood, which threatening local lives and arousing public evacuation. If people underestimate the rainfall, potential flood would be underrated; while estimmatng the rainfall would lead to unnecessary evacuation orders and flood protection, which is also costly.
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3.2
DATASET STATISTICS
151 At the time of this work, we have collected and processed precipitation data for the rainy season for 17 years from 2002 2018. One precipitation map pair per hour, 24 precipitation map pairs per day. In detail, we have collected 85 months or 62424 hours, totaling 62424 pairs of high- resolution and low-resolution precipitation maps. The size of the high-resolution precipitation map is 624 x 999, and the size of the low-resolution is 208 333. Various meteorological phenomena and precipitation conditions (e.g., hurricanes, squall lines, e.f.c.) are covered in these data. The precipitation map pairs in RainNet are stored in HDF5 files that make up 360 GB of disk space. We select typical meteorological phenomena and visualize them in Fig. [ Our data is collected from satellites, radars, gauge stations, e.t.c., which covers the inherent working characteristics of different meteorological measurement systems. Compared with traditional methods that generate data with different resolutions through physical model simulation, dataset is of great help for deep models to learn real meteorological laws.
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DATASET ANALYSIS
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In order to help design a more appropriate and effective precipitation downscaling model, we have explored the property of the dataset in depth. As mentioned above, our dataset is collected from mul- tiple sensor sources (e.g.. satellite, weather radar, e.t.c.), which makes the data show a certain extent of misalignment. Our efforts here are not able to vanquish the misalignment. This is intrinsic
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problem brought by the fusion of multi-sensor meteorological data. Limited by observation meth- ods (e.g., satellites can only collect data when they fly over the observation area), meteorological data is usually temporal sparse, e.g., in our dataset, the sampling interval between two precipitation maps is one hour. The temporal sparse leads to serious difficulties in the utilization of precipitation sequences. Additionally, the movement of the precipitation position is directly related the cloud. It is a fluid movement process that is completely different from the rigid body movement concerned in resor-Resolution. At the same time, the cloud will grow or dissipate in the process of flowing and even form new clouds, which further complicates the process. In the nutshell, although existed SR a potential solution for downscaling, there is a big difference between the two. Especially,
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the three characteristics of downscaling mentioned above: temporal misalignment, temporal spurse, fluid properties, which make the dynamic estimation of precipitation more challenging.
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EVALUATION METRICS
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Due to the difference between downscaling and traditional figure super-resolution, the metrics that work well under SR tasks may not be sufficient for precipitation downscaling. By gathering the metrics from the meteorologic literature (the literature includes are (Zhagg& &
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Maraun et al. (211)): Ekström (2016); He et al. (2016): Pryor & Schoof (020); Wootten et al. (2020), select and rename 6 most common metrics (a metrics may have multiple names in different literature) to reflect the downscaling quality: mesoscale peak precipitation error (MPPE), cumulative precipitation mean square error (CPMSE), heavy rain region error (HRRE), cluster mean distance
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(CMD), heavy rain transition speed (HRTS) and average miss moving degree (AMMD). These 6 metries can be separated as reconstruction metrics: MPPE, HRRE, CPMSE, AMMD, and dynamic metries: HRTS and CMD.
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The MPPE (mm//hour) is calculated as the difference of top quantile between the generated/real rainfall dataset which considering both spatial and temporal property of mesoscale metcorological
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systems, e.g., hurricane, squall. This metric is used in most of these papers (for example Zhhag
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& Yangl (2004); Maraun et al. (2015); Ekström (016);; He et al. (006)); Pryor & Schoof (020);;
Wootten et al. (2020) suggest the quantile analysis to evaluate the downscaling quality).
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The CPMSE (mm²² /hour?) measures the cumulative rainfall difference on each pixel over the time- axis of the test set, which shows the spatial reconstruction property. Similar metrics are used in
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Zhang & Yang (2004); [Maruu et al. (2015); Wootten et al. (2020) calculated as the pixel level difference of monthly rainfall and used in He et al. (2016) as a pixel level difference of cumulative
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rainfall with different length of record.
The HRRE (km²) measures the difference of heavy rain coverage on each time slide between gen- erated and labeled test set, which shows the temporal reconstruction ability of the models. The
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AMMD (radian) measures the average angle difference between main rainfall clusters. Similar
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metrics are used in Zhang & Yang) (2004); Maraun et al.) (2013); Wootten al. (2020) rainfall
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coverage of a indefinite number precipitation level and used in He et al. (2016): ; Pryor & SSchoof
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2020 as a continuous spatial analysis.
206 As a single variable dataset, is hard to evaluate the ability of different models to capture the precipitation dynamics when temporal information is not included (a multi-variable dataset may have wind speed, a typical variable representing dynamics, included). So here we introduce the first-order temporal and spatial variables evaluate the dynamical property of downscaling results.
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Similar approaches are suggested in Maraun et al. (0155) Ekström Pryorr & Schoof (2020).
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The CMD (km) physically compares the location difference of the main rainfall systems between the generated and labeled test set, which could be also understand as the RMSE of the first order derivative of precipitation data on spatial directions.The The HRTS (km/ fem/hour) measures the difference between the main rainfall system moving speed between the generated and labeled test set which shows the ability for models to capture the dynamic property, which could be also understand as the RMSE of the first order derivative of precipitation data on temporal Pirection.Simile metrics are suggested in Maraun et al. (2015); |Ekström (2016): Pryor & Schoof (020) as the auto-regression
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analysis and the differential analysis.
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More details about the metrics and their equations are given in supplementary materials. One met- rics group (MPPE, HRRE, CPMSE, AMMD) mainly measures the rainfall deviation between the
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Ground Truth 1
UpSampling
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Figure 2: The pipeline of our proposed baseline model for spatial precipitation downscaling.
generated precipitation maps and GT. The other group (HRTS and CMD) mainly measures the dynamic deviation of generated precipitation maps. order to further simplify the application of indices, we abstract them into two weighted and summed metrics: Precipitation Error Mea- sure (PEM) and Precipitation Dynamics Error Measure (PDEM). We first align the dimensions of these two groups of metrics respectively. The first group of metrics (MPPE, HRRE, CPMSE, AMMD) is normalized, weighted and summed to get the precipitation error measure (PEM). Ac-
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cording Gupta et al. (1999), all the metries transferred to Percent Bias (PBIAS) to be suit- able for metrics weighting. The original definition of PBIAS the bias divided by observation, as mQ Q/nal/ Qohal- Here rewrite the original metrics to PBIAS by dividing the metries with annual mean observations of the original variables (AMO), as PBIASPEN PRRE,CPMSE AMMD). In our CPMSE = 0.64, AMO single metric (PEM) with equal weight, as PEM E, 0.25 PBIASPEM Following the same procedure,
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PBIAS
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M M PEM //AAMO |Metrics PEM PEM = (MPPF,
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dataset, AMOPEM PEM
MPPE = 64, AMOPEM AMOPEM =1,, AMO HRREM = 533, AMM
CMD 26. The metrics then are ensembled AMMD 332,
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we then ensemble the second group of dynamic metrics (HRTS and CMD) to a single metrics
PDEM E, 0.5 PBIASP
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We also include the most common used metrics RMSE as one single metrics in our metrics list. RMSE could evaluate both reconstruction and dynamic property the downscaling result.
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APPLICATIONS OF RAINNET IN SPATIAL PRECIPITATION DOWNSCALING
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As potential solution, Super-Resolution (SR) frameworks generally divided into the Single- Image Super-Resolution (SISR) and the Video Super-Resolution (VSR). Video Super- Resoo-Reso is able leverage multi-frame information to restore images, which better matches the nature of down- scaling. We will demonstrate this judgment in Sec. 6.1] The VSR pipeline usually contains three components: deblurring, inter-frame alignment, and super-resolution Deblurring and inter-frame alignment are implemented by the motion estimation module. There are four motion estimation frameworks: 1). RNN based (Keys (1981); 1) Tao al. (2017): [Huang et al.] (2015); Haris et al. ):19): 2). Optical Flow (Xue et al. (2019); 3). Deformable Convolution based (Tian et al. (2020);
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Xiang et al. (2020): Wang et al. (201)): 4). Temporal Concatenation (Jo et al. (2018): Caballero al. (2017): Liao al. (2115)) In fact, there is another motion estimation scheme proposed for the first time in the noise reduction task (Tassano et al. (2020), which achieves an excellent video noise reduction performance. Inspired by (Tassano et al. (020)). ) design implicit dynamics estimation model for the spatial precipitation downscaling. It is worth mentioning that our proposed
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model and the above four frameworks together form relatively complete candidate of dynamic estimation solutions.
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Proposed Framework. As shown in Fig. 2 our framework consists two components: Implicit dynamic estimation module and downscaling Backbone. These two parts are trained jointly. Suppose there N adjacent low-resolution precipitation maps {16 N-1 .., 14 1 N-1 ). The task is to
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reconstruct the high-resolution precipitation map 1/ of If. The implicit dynamic estimation module is composed of multiple vanilla networks A {A1, .... AN-2) (N 5 in this paper) sharing weights. Each vanilla network receives three adjacent frames as input, outputs, and intermediate results. The intermediate result be considered as frame with implicit dynamic alignment. We
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concatenate all the intermediate frames as the input of the next module. The specific structure of the vanilla network can be found in the supplementary materials. The main task of the downscaling backbone is to restore the nigh-resolution precipitation map 1/ based the aligned intermediate frames. In order to make full use of multi-scale information, we use multiple Residual-in-Rede Dense Blocks (Wang et al. (201)) in the network. We employ the intepollaionncconlllttio (Odena
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et al. (2016)) as the up-sampling operator to reduce the checkerboard artifacts. After processing by
downscaling backbone we get the final estimated HR map 14
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Model objective. The downscaling task is essentially restore high-resolution precipitation maps.
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We learn from the
task and also apply L1 and perceptual loss (Johnson et al. (211))
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as the training loss of our model. The model objective is shown below:
(1) where & denotes the pre-trained VGG19 network (Simonyan & Zisserman (0155), we select the Relu5 4 (without the activator (Wang al. (2018)) as the output layer. À is the coefficient to
- + 6 (( 112.
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balance the loss terms. 20 in our framework.
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EXPERIMENTAL EVALUATION
We conduct spatial precipitation downscaling experiments illustrate the application of our proposed RainNet and evaluate the effectiveness of the benchmark downscaling frameworks. Fol- lowing the mainstream evaluation protocol of DL/ML communities, cross-validation is employed. In detail, we divide the dataset into 17 parts (2002. 10002.7-~000.11, 2003. 200
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2008.7~2008.11. 2009.7~~2099.11, 2005.7~2005.11, 2006.7>~206.111 11, 2007.7~2007.11,
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2010.7~~010.11, 2011.7~2011.11, 2012.7-2012.11, 2013.7-2013.11, 2014.7--001..11
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2015.7-2015.11, 2016.7~2016.11 2017.7-2017.11, 2018.7-2018.11) by year, and sequentially employ each year the test set and the remaining 16 years as the training set, that is, 17-fold cross-valin cross-validation. All models maintain the same training settings and hyperparameters during the
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training phase. These data cover various complicated precipitation situations such hurricanes, squall lines, different levels of rain, and sunny days. It is sufficient to select the rainy season of the year as the test from the perspective of meteorology, the climate of one area is normally stable
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BASELINES
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The SISR/VSR and the spatial precipitation down- scaling are similar to some extent, so we argue that the SR models can be applied to the task as the
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benchmark models. The input of SISR is a single
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image, and the model infers a high-resolution image from it. Its main focus is to generate high-quality texture details to achieve pleasing visual effects. In contrast, VSR models input multiple frames of im-
Homey min Immittin geed
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ages (e.g.. 3 frames, 5 frames, e.t.c).. In our experi- ments, we employ 5 frames. The core idea of VSR models to increase the resolution by complement-
SRCAN
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ing texture information between different frames. It is worth mentioning that VSR models generally are equipped with a motion estimation module alle-
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viate the challenge of object motion to inter-frame information registration.
Figure 3: The dynamic property of bench- mark algorithms. The frameworks of VSR are gathered the lower-left corner of the figure, which demonstrates that VSR meth- ods are superior to SISR and traditional methods in dynamic properties.
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(i.e.. Bicubic Keys (1981b). SRCNN (Dong
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et al. 2016 SRGAN Ledig et al. (0017).

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Approach
MPPF4
HRREL
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CPMSEJ
HRTS!
CMD
PEML
P
RMSEx 1004
Kriging
4.036
339.641
0.204
4.891
9.958
12.277
0.259
0.568
0.372
Bicubic
4.600
306.996
0.208
3.678
10.453
12.389
0.247
0.587
0.345
SRCNN
5,333
296.950
0.225
3.929
10.091
12.396
0.252
0.575
0,405
SRGAN
14.125
298.290
0.221
91.464
9.429
11.891
0.352
0.543
0.603
EDSR
4.748
288.354
0.204
3.292
9.605
12.259
0.236
0.556
0.329
ESRGAN
6,205
407.848
0.219
4.483
10,201
17.035
0.305
0.668
0,563
DBPN
6.596
302.278
0.212
5.692
9.869
11.336
0.256
0.547
0.380
RCAN
4.709
272.189
0,200
3.062
9.772
12.055
0.227
0.558
0.325
SRGAN-V
10.007
291.546
0.210
35,932
8.276
10.448
0.286
0.477
0,557
EDSR-V
4.592
289.331
0.201
3.269
8.484
11.214
0.235
0.498
0.323
ESRGAN-V
7.187
413.398
0.213
4.010
7.887
10.695
0.309
0.469
0.399
RBPN
4,816
287.214
0.201
2.680
8.267
11.244
0.235
0.492
0.317
EDVR
2.148
213.034
0.179
1.352
8.479
10.060
0.180
0.476
0.329
Ours
4.198 221.859
0.191
1.890
7.723
9.568
0.197
0.441
0.312
Cross-validation results. Comparison with state-of-the-art super resolution approaches. The best performance is marked with red (Ist best), blue (2nd best).
Table 1:
EDSR
Lim et al. 2017 ESRGAN Wang et al. (2018)) DBPN Haris al. 2018
RCANG (Zhang et al. (2018) and VSR frameworks (i.e SRGAN-V. EDSR-V, ESRGAN-V.
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RBPN Haris et al. (2019). EDVR (Wang al. 2019)). of which VSR methods (i.e. SRGAN-
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V. EDSR-V, ESRGAN-V) are modified from SISR. In particular, we build SRGAN-V, EDSR-V and
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ESRGAN-V by concatenating multiple frames of precipitation maps as the input of the model. In
addition, we also evaluated the traditional statistics method Kriging (Stein (2022), which is widely applied in weather forecasting. The mentioned 8 metrics are used quantitatively evaluate the performance of these SR models and our method. Further, we select some disastrous weather as samples for qualitative analysis to test the model's ability to learn the dynamic properties of the weather system. And employ the implementation of Pytorch for Bicubic. We use 4 NVIDIA 2080 Ti GPUs for training. We train all models with following setting. The batch size is set as 24. Precipitation maps random crop into 64 x 64. We employ the Adam optimizer, betal is 0.9, and beta2 is 0.99. The initial learning rate is 0.001, which reduced to 1/10 every 50 epochs, and a total of 200 epochs trained. We evaluate benchmark frameworks with 17-fold cross-validation. The downscaling performances are shown in Tab. [ We divide the indicators mentioned above into two groups PDEM measures the model's is ability learn the dynamics of precipitation. PEM illustrates the model's ability to reconstruct precipitation.
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From Tab. I we can learn that the overall performance of the VSR methods are better than SISR models, which shows that the dynamic properties mentioned above are extremely important for the downscaling model. Furthermore, it can be seen from Fig. 3 that the SISR method is clustered in the
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upper right corner of the scatter plot, and the VSR method is concentrated in the lower-left corner, which further shows that the dynamic properties of the VSR methods are overall better than the SISR methods. In addition, our method achieves the ss/ best performance in RMSE, PDE, and achieve the second-best performance on PEM. The score shows that the implicit dynamic estimation framework
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PyTTrhh
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LR(green) & HR
Bicubic
SRCNN
SRGAN
EDSR
ESRGAN
DBPN
RCAN
SRGAN-V
EDSR-Y
ESRGAN-V
RBPN
EDVR
DUF
Ours
Figure 4: Visual comparison with ofate-of-the-aar Super Resolution approaches. Please zoom-in the figure for better observation. More results can be found in suppl.
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used is feasible and effective. is worth mentioning that the traditional downscaling method Kriging performs better than many deep learning models (e.g., SRGAN, ESRGAN)
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6.
1.1
QUALITATIVE ANALYSIS
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We visualized the tropical cyclone precipitation map of the 166th hour (6th) in September 2010 and the high-resolution precipitation map generated by different methods. As shown in Fig. the best perceptual effects are generated by EDVR and Our framework. Zooming in the result image,
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339 the precipitation maps generated by SRGAN and EDSR present obvious checkerboard artifacts. The reason for the checkerboard artifacts should be the relatively simple and sparse texture pattern 341 in precipitation maps. The results generated by Bicubic, RCAN, Kriging, and SRCNN are over- smooth. DBPN even cannot reconstruct the eye of the hurricane. Especially, the result generated by Kriging is as fuzzy as the input LR precipitation map. In conclusion, the visual effects generated by the VSR methods generally better than the SISR methods and the traditional method. From the perspective of quantitative and qualitative analysis, the dynamics estimation framework is very critical for downscaling.
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7 CONCLUSION
In this paper, built the first large-scale real precipitation downscaling dataset for the deep learning community. This dataset has 62424 pairs of HR and LR precipitation maps in total. We believe this dataset will further accelerate the research on precipitation downscaling. Furthermore, we analyze the problem in-depth and put forward three key challenges: temporal misalignment, temporal sparse, fluid properties. In addition, we propose an implicit dynamic estimation model to alleviate the above challenges. At the same time, we evaluated the mainstream SISR and VSR models and found that none of these models can solve RainNet's problems well. Therefore, the downscaling task on this dataset is still very challenging.
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This work still remains several open problems. Currently, the data domain this research limited to the eastern U.S. In future research, would enlarge the dataset to a larger domain. The dataset only a single variable now. In future research, we may include more variables, e.g. temperature and wind speed.
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Reviews:
[{"review_id": "rllH4EXlkc3", "reviewer": "Reviewer_ggKX", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strength: \n- Creates and releases a high/low res pair dataset of real images which are ML-ready and instrumental in kicking of climate research. \n- Provide well thought out experiments (reproducing other methods etc) along with their baselines on their new metric\n\nWeakness: \n- One way to evaluate how much better are high res images is to use them for potential Applications to downstream tasks which is not provided. \n- Any method that converts low res to high res, generates some information and so uncertainty maps help instill confidence in predictions. The paper does not provide any uncertainty estimation. \n- Qualitative analysis of methods such as visually seeing their utility could help a domain scientist use this method; but qualitative analysis is lacking. ", "summary_of_the_paper": "The paper provides a baseline method that generates a dataset of high+low res precipitation maps. The paper argues that precision of weather event predictions depend on the imagery resolution and hence having high res maps will lead to better weather prediction.  The eventual dataset that is formed contains a variety of diverse climate events like hurricanes etc and the pair of high/low res forms the necessary annotation. ", "summary_of_the_review": "Overall well written. As the authors mention Ml-readiness; its also important to think about how will an actual domain scientist use this work. This is where evaluating downstream applications and understanding uncertainty in the results is crucial. This part is missing, other than that its a clear and concise approach. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "__VhkPIxSfH", "reviewer": "Reviewer_5pVg", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The topic of the paper is relevant -- a large scale dataset of gridded, paired, high-resolution and low-resolution precipitation measurements would be both interesting and highly useful for exploring deep learning approaches to for earth science applications. However, this paper is lacking in several key dimensions:\n\n- First, the paper does not adequately describe the proposed dataset itself:\n  - From Section 3.1 it seems that the data comes from the NLDAS dataset and the NCEP Stage IV 4km gridded precipitation data. More details are needed here, for example, the NLDAS dataset has two versions (001 having been discontinued in April 2020). Which products exactly were used? What are the limitations of these datasets (e.g. NLDAS is a reanalysis dataset)?\n  - The resolution of the high-resolution data is 0.04 degrees and the resolution of the low-resolution data is 0.125 degrees, however the high-resolution and low resolution images are exactly 3x different, which implies that a resampling step was used at some point and should be detailed.\n  Why were image sizes of 624x999 and 208x333 used? Practically, in most deep learning cases these will need to be cropped/resized anyway to accommodate downsampling.\n  - Figure 1 seems to show that the low resolution data is clipped to the coastline? If this is the case, then it would present a serious limitation of the dataset as the high-resolution data looks like it is _not_ clipped to the coast.\n  - The paper argues that this is the first _real_ dataset for studying the downscaling task, but it does not argue why it is insufficient to simply downsample the high-resolution data to get low-resolution data. If it is the case that there is a large difference between the downsampled high-resolution data and the low-resolution data, then this should be discussed. \n- Second, the reported results (Table 1) are from a 17 fold cross-validation procedure over time, however are shown without standard deviations or confidence intervals. The difference between some methods is very small, and could easily be insignificant. Deep learning approaches can have large differences in performance with everything but random number seed used in training held the same, therefore it is important to at least report standard deviation over runs. As is, it is difficult to draw conclusions about the quality of the different approaches.\n- Finally, there are many claims and details throughout the paper that need to be cited/addressed. For example:\n  - The first sentence of Section 2 is incorrect. Bauer et al. (2015) explain that the beginning of the 20th century (not the beginning of the 19th century as written in line 89) is when \"geoscientists recognized that predicting the state of the atmosphere could be treated as an initial value problem...\". The Navier-Stokes equations were only discovered in the mid-19th century.\n  - (Line 252) \"It is worth mentioning that our proposed model and the above four frameworks together form a relatively complete candidate set of dynamic estimation solutions.\" -- this is a strong, unnecessary, claim without any support -- how do the proposed model and four frameworks form a relatively complete set of dynamic estimation solutions?\n  - Lines 131 and 133 includes \"GNU Free Documentation License 1.2\" completely out of context?\n  - \"The precision of weather and climate prediction is highly dependent on the resolution and reliability of the initial environmental input variables, and spatial precipitation downscaling is the most promising solution.\" -- this is a bold claim and needs a citation.\n  - \"The error of weather forecast would increase exponentially over time from this initial error of input dataset.\" (lines 105-106) the error _does_ increase exponentially over time.\n\nMinor points:\n- \"e.t.c.\" --> \"etc.\" throughout\n- \"Unfortunately, there are looming issues hinders the\" --> \"Unfortunately, there are looming issues that hinder the\"\n- \"Rainnet\" --> \"RainNet\" throughout\n- \"quantile-quantile maps to analyzing the maps.\" (lines 56-57) doesn't make sense\n- \"Hurricane, Squall\" --> \"hurricane, squall\" (line 59)\n- \"alleviate these obstacles\" consider rewording \"alleviate these challenges\", \"overcome these obstacles\"\n- \"REAL\" --> \"real\" (line 82)\n- Sentence incomplete (line 104-105)\n- Caption of Figure 1 says, \"To learn more about the dataset, please visit our project website (coming soon) and supplementary material.\" The main contribution of the paper, however, is this dataset, therefore the caption should reference the paper.\n- Line 219 says more details about the metrics is given in the supplementary materials, however the supplementary materials first copies the text from the main paper word-for-word (including the sentence about referencing the supplementary material...)\n- The list of dates in line 279-282 should be described differently.\n- In the Proposed Framework subsection (line 255) H, L, and T are not described.\n- Consider using font formatting instead of coloring in Table 1 to make the table more easily interpreted by readers with colorblindness. The red highlighting, in particular, will be difficult for readers with the most common form of colorblindness.\n- Figure 4, caption is potentially unfinished. ", "summary_of_the_paper": "This paper proposes a dataset called RainNet for studying precipitation downscaling. RainNet consists of ~62k pairs of low-res/high-res precipitation maps from various meteorological events over the east coast of the US over 17 years. This dataset was created from two existing products: NLDAS and the NCEP's Stage IV 4km gridded precipitation data. Further, the paper proposes two metrics, Precipitation Error Measure (PEM) and Precipitation Dynamics Error Measure (PDEM). Finally, the paper compares several superresolution methods from computer vision literature, traditional statistical approaches to downscaling, as well as the proposed architecture/method on the proposed dataset to form a set of benchmark results.", "summary_of_the_review": "This paper should be rejected. Its main contribution is the dataset, RainNet, and benchmark results on RainNet. However the paper does not adequately describe the dataset, and the benchmark results are not reported with measures of uncertainty, which makes comparisons impossible.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "ZcdWkYYedr6", "reviewer": "Reviewer_szBD", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "There is undoubtedly a need for large labeled and trustworthy datasets for segmentation, detection, tracking of large-scale weather events including extreme weather events. From that perspective, this paper is a good contribution to the field of AI-driven meteorology where such datasets are in abundant need. The evaluation of the different models on this dataset provides a good benchmark as well. This is well written and deserves to be published although I'm unsure whether it should be in ICLR. I feel that a more meteorology-focused journal is a better venue for this work.", "summary_of_the_paper": "This paper presents a $0.4^\\circ$ dataset for precipitation downscaling. It further shows 14 SOTA models with/without ML for evaluation on the dataset. ", "summary_of_the_review": "As I mentioned in my review, this is a great paper proposing a new dataset that is well constructed and evaluated and is high-resolution. However, I don't feel that ICLR is the right venue for this work. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}, {"review_id": "KwoC8LrThp", "reviewer": "Reviewer_D3tQ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "This work was recently submitted to NeurIPS 2021. ~~I would highly recommend that all reviewers and area chairs read the discussion there and compare the previously submitted version of this work to the version that is under review here.~~\n\nEDIT: Unfortunately, NeurIPS changed their visibility policy in 2021 for rejected submissions, so the discussion is no longer available to the public. Apologies for the confusion.\n\nThe authors have made a few improvements to address the concerns raised in this original discussion:\n\n1) Cross validation results are presented to provide a more robust comparison between models.\n2) The discussion of metrics in section 4 is much more thorough than in the previous version.\n\nUnfortunately, several issues remain unaddressed.\n\n1) **Quantitative results do not include standard errors or any indication of the variance in cross validation.** This was one of the top concerns raised in the previous discussion. The per-fold results posted by the authors in this thread show a substantial amount of variance in the relative ranking of the evaluated methods between folds, so this is very important for transparency and fair model comparison.\n2) **Inadequate discussion of hyperparameter tuning, particularly for the baselines.** The description of hyperparameter settings improved in this version of the work, but the discussion on how hyperparameters were selected did not. There is no discussion of hyperparameter tuning methodology and/or how the hyperparameters for baseline methods were chosen. It is both unfair and uninteresting to compare a model which was tuned for a statistical downscaling task to a baseline which was tuned for natural image super resolution.\n3) **Inadequate framing of work in context of existing literature.** The literature review is sparse and does not provide any discussion of how the authors work fits into the broader context of the literature on applying deep learning to statistical downscaling. There is also no discussion of existing frameworks often used for downscaling evaluation such as VALUE and Climdex (citations are provided but there are no mentions in the text).\n4) **Poor presentation of qualitative results.** Figure 4 in particular is difficult to evaluate due to the poor rendering quality and presentation format. As mentioned in the previous discussion, the low/high resolution ground truth are too small relative to the other images for meaningful visual comparison.\n5) **Language and formatting errors.** The text is still littered with typos, grammatical errors, and problematic (sometimes even confusing) diction. Several such issues that were pointed out directly by reviewers in the previous discussion still remain, which seems to indicate that the authors did not bother to further proof-read or revise the text.", "summary_of_the_paper": "The authors present a novel, high resolution dataset for precipitation downscaling collected from observational data in the southeastern US. In addition to the dataset, several novel metrics for evaluating statistical downscaling methods are presented, as well as a novel deep learning algorithm based on video super resolution and \"implicit dynamics\" estimation.", "summary_of_the_review": "The dataset and metrics presented by the authors are valuable contributions, but the paper has significant problems with framing, presentation, quantitative evaluation, and language/formatting. Due to these quality deficiencies, I cannot recommend acceptance of this paper in its current form.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a new dataset, called RainNet, obtained from gridded precipitation data, for training precipitation downscaling methods, as well as a new neural network-based architecture for that task, which estimates the underlying dynamics of the local weather system, and new metrics for evaluating precipitation downscaling methods.

Reviewers praised the large, novel and useful dataset (D3tQ, szBD, ggKX) and novel metrics for evaluating statistical downscaling methods (D3tQ), along with evaluation on 14 baselines (szBD, ggKX).

There were however many issues highlighted by the reviewers. First, reviewer D3tQ raised concerns about the paper being resubmitted after rejection from NeurIPS (/pdf?id=VVZZJiQB51l), with minimal changes (/pdf?id=6p8D4V_Wmyp), and noticed that the authors did not follow up on most reviewer recommendations. D3tQ noticed however that in the ICLR resubmission, the cross validation results were presented to provide a more robust comparison between models, and that the discussion of metrics in section 4 was much more thorough than in the previous version.

Other themes in the negative reviews included concerns about missing standard errors in the cross-validation results (D3tQ, 5pVg) or measures of uncertainty in the upscaling (ggKX), lack of information about hyperparameter tuning (D3tQ), inadequate literature review about statistical downscaling (D3tQ), lack of information about the dataset (5pVg), missing discussion about applications (ggKX) and insufficient proofreading (D3tQ, 5pVg).

I will not take into consideration the criticism from szBD who "don't feel that ICLR is the right venue for this work" as I do not find such opinions to be much helpful.

The authors did not provide a rebuttal to the initial reviews and there was no discussion about this paper among the reviewers. Given the issues raised by the reviewers and the scores of 3, 3, 5 and 6, I believe that this paper does not meet the acceptance bar in its current form.

Sincerely,
AC
Decision: Reject