PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling
Authors: Junchao Gong
Siwei Tu
Weidong Yang
Ben Fei
Kun Chen
zhangwenlong
Xiaokang Yang
Wanli Ouyang
LEI BAI
Venue: ICLR 2025 Poster (2025)
Keywords: AI for Science; Precipitation Nowcasting; Diffusion Model; Zero-shot Blurriness Kernel; Auto-scale Denoise Guidance
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Precipitation nowcasting plays a pivotal role in socioeconomic sectors, especially in severe convective weather warnings. Although notable progress has been achieved by approaches mining the spatiotemporal correlations with deep learning, these methods still suffer severe blurriness as the lead time increases, which hampers accurate predictions for extreme precipitation. To alleviate blurriness, researchers explore generative methods conditioned on blurry predictions. However, the pairs of blurry predictions and corresponding ground truth need to be given in advance, making the training pipeline cumbersome and limiting the generality of generative models within blurry modes that appear in training data. By rethinking the blurriness in precipitation nowcasting as a blur kernel acting on predictions, we propose an unsupervised postprocessing method to eliminate the blurriness without the requirement of training with the pairs of blurry predictions and corresponding ground truth. Specifically, we utilize blurry predictions to guide the generation process of a pre-trained unconditional denoising diffusion probabilistic model (DDPM) to obtain high-fidelity predictions with eliminated blurriness. A zero-shot blur kernel estimation mechanism and an auto-scale denoise guidance strategy are introduced to adapt the unconditional DDPM to any blurriness modes varying from datasets and lead times in precipitation nowcasting. Extensive experiments are conducted on 7 precipitation radar datasets, demonstrating the generality and superiority of our method.

Published as a conference paper at ICLR 2025
POSTČAST: GENERALIZABLE POSTPROCESSING FOR PRECIPITATION NOWCASTING VIA UNSUPERVISED BLURRINESS MODELING
Junchao Gongl.2; Siwei Tu Weidong Yang2.;; 3; Ben Fef2. Kun Chen Wenlong Zhang* Xiaokang Yang'. Wanli Ouyang², Lei Bai
'Shanghai Jiao Tong University PShanghai Artificial Intelligence Laboratory FFudan University "The Chinese University of Hong Kong
ABSTRACT
Precipitation nowcasting plays a pivotal role in socioeconomic sectors, espe- cially in severe convective weather warnings. Although notable progress has been achieved by approaches mining the spatiotemporal correlations with deep learn- ing, these methods still suffer severe blurriness as the lead time increases, which hampers accurate predictions for extreme precipitation. To alleviate blurriness, re- searchers explore generative methods conditioned on blurry predictions. However, the pairs of blurry predictions and corresponding ground truth need be gener- ated in advance, making the training pipeline of generative parts cumbersome and limiting the generality generative models within blur modes that appear training data. By rethinking the blurriness in precipitation nowcasting as a blur kernel acting on predictions, we propose an alternative postprocessing method to eliminate the blurriness without the requirement of training with the pairs of blurry predictions and corresponding ground truth. Specifically, we utilize blurry predictions to guide the generation process of pre-trained unconditional denois- ing diffusion probabilistic model (DDPM) to obtain high-fidelity predictions with eliminated blurriness. A zero-shot blur kernel estimation mechanism and an auto- scale denoise guidance strategy are introduced to adapt the unconditional DDPM any blurriness modes varying from datasets and lead times in precipitation now- casting. Extensive experiments are conducted on 7 precipitation radar datasets, demonstrating the generality and superiority of our method. Our code available at httttps:// cwm/coomsssss.e.eeeeee...eeeeeee jasong ove PPop/ossstttt
com/
INTRODUCTION
Precipitation nowcasting, which mostly depends on radar echo data, plays a vital role in predicting local weather conditions for up to six hours (CLIMA & TE). Accurately predicting precipitation events one of the core tasks weather prediction. could mitigate the socioeconomic impacts of extreme precipitation events and serve as a critical tool for transportation management, agricultural productivity, and other aspects. Hence, many excellent methods have been proposed in recent years.
Traditional methods for radar-based precipitation nowcasting rely statistical models and physi- cal assumptions (del Moral et al., 2018 Woo & Wong 2017). Although these methods have the advantage of computational efficiency and high explainability, the chaotic and nonlinear nature of short-term precipitation means that various physical and statistical assumptions introduced in tradi- tional methods have inherent limitations. These methods only suitable for cases with smooth and simple motion patterns over short periods. Therefore, researchers explore the use of deep learning to mine the spatiotemporal correlations in precipitation nowcasting. These methods treat precipitation as task of spatiotemporal prediction, predicting future radar echoes given the sequence of historical observations. By designing modules better model the spatiotemporal dynamics in precipitation nowcasting, many attempts have provided solid improvements in the evaluation of Critical Success
"Equal Contribution
I "orresponning Authors: Ben Fei (benfei@cuhle www.ucc..........coaoaaao cdu.hk) and Lei Bai (bailei Mialei@@jlaane (aaili@@jllabbor..ccm)

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Published as a conference paper at ICLR 2025
Groundrrsh snyprrddiiie
Spatictemporal
Conditional generative models
Uncondictional DDPM
prediction modells
Staget: generating (GT, Blurry prediction) paris
Stage2: supervised training with generated pains
Directly training with ground
Figure 1: Left: Previous methods require two stages to generate predictions with local weather patterns, which generate (GT, blurry prediction) pairs in stage I and apply these pairs to supervise the training of conditional generative models in stage Right: We propose to directly train an unconditional DDPM to convert blurry predictions into the distribution of ground truths.
Index (CSI) (Shi al. 2015 Wang al. 2022 Gao al., 2022b a). However, they often suffer from severe blur when the lead time of predictions increases (Gong et al.). Such blur hinders the predictions from containing local patterns that represent small-scale weather systems which are usu- ally correlated to extreme precipitation events (Ravuni et al., 202Ip. Over the past 50 years, these extreme precipitations have caused 1.01 million related deaths, and over US$ 2.84 trillion economic losses. It results in continuous efforts to mitigate the blur in long-term predictions (Douris & Kim 2021).
To avoid blurry predictions, researchers have proposed to utilize probabilistic methods to generate future radar echoes. Probabilistic methods, such as generative adversarial networks (GAN) or dif- fusion models (DM), sample different latent variables to express the stochasticity of chaotic future weather systems, enabling them to capture local weather patterns instead the smooth mean value predicted in deterministic methods mentioned above (Ravuri al., 202T Gao et al. 2024 Zhao et al. 2024b). Furthermore, to simultaneously take advantage of probabilistic and deterministic modeling, recent methods utilize blurry predictions capture the global movement of precipitation cloud clusters and harness probabilistic components to predict small-scale systems (Gong et al.) Yu al., 2024 Zhang et al.) 2023). However, these deterministic and probabilistic coupling meth- ods also have several challenges. These methods train separate probabilistic models for different datasets, different deterministic models, and different lead times, which hampers the generalization capabilities of models. For instance, these methods require retraining when transferred from Shang- hai to Hong Kong, as the deterministic models and observations applied by the local Meteorological Bureaus are different. Second, the training process of the probabilistic part of the coupling meth- ods is complex. To train the probabilistic component, the blurry predictions and the corresponding ground truth are required to be provided in advance (Gong al.; Yu et al. 2024 Zhang et al. 2023). As a result, there is an additional stage to prepare blurry predictions for the probabilistic model shown in Figure In summary, the generalization and flexibility of the deterministic and probabilistic coupling methods is limited.
Instead of capturing local patterns by the coupling training methods, we propose rethink the blurry predictions from a direct perspective. The blurry predictions could be recognized as the results of blur kernels acting on the predictions with the distribution real-world data. As shown in Appendix A.2, the blur kernels K.S.T.M are related to sample S, lead time of predictions T' and deterministic model M. Thus, we could obtain predictions without blurriness by solving the inverse of blur kernel K.S.T.M- This perspective can lead to a totally different training paradigm to recover local weather patterns.
Motivated by the idea of deblurring, we propose our PostCast. The blur kernel K.S.T.M .M can be ob- tained by unsupervised estimation, which alleviates the burden of generating blurry predictions by complex spatiotemporal modeling. Besides, the process of obtaining the inverse solution of blur kernel K...T.M is generalizable, enabling our method to be flexibly applied in various datasets, de- terministic models, and time steps. Specifically, our PostCast is unified framework that integrates the generative prior inherent in the pre-trained diffusion model with zero-shot blur kernel estima- tion mechanism and auto-scale denoise guidance strategy to tackle blurry predictions across various datasets, models, and prediction lengths. Firstly, we utilize the pre-trained unconditional diffusion model on ImageNet from (Nihhll & Dhariwal 2021) for better initialization and fine-tune this dif-
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fusion model on five precipitation datasets to enrich it with generative prior that can be utilized to generate high-quality precipitations. After the unconditional diffusion model is finetuned, we can utilize blurry predictions to guide the sampling process. In every sampling step, the diffusion model first generates a clean precipitation image So from the noisy precipitation image zi by estimating the noise in zt.- We can add guidance with blurry predictions given by spatiotemporal prediction models on this intermediate variable To to control the sampling process of the diffusion model. Since blurry prediction undergoes unknown degradation, zero-shot blur kernel estimation mechanism and an auto-scale denoise guidance strategy are formulated to adaptively simulate this unknown degradation by kernel K.TT.M at any blur modes. The parameters of the optimizable blur kernel are randomly initialized and optimized by the gradient of the distance metric between blurry prediction and the in- termediate variable T. after the optimizable blur kernel. In this way, clean precipitation predictions guided by blurry predictions will be obtained after the sampling process of the diffusion model. Ad- ditionally, our method could also obtain the blur kernels that convert clean precipitation predictions into blurry ones, demonstrating the effectiveness of our optimized blur kernel. We demonstrate that our PostCast enhances the blurry predictions of existing methods on several precipitation datasets. Moreover, our PostCast can be adapted to wide range of sample S, lead time of predictions T, and deterministic model M.
2 RELATED WORK
2.1
PRECIPITATION NOWCASTING
Notable progress has been achieved by applying deep learning in precipitation nowcasting (Chen
et al., 2023 Han et al. 2024b; Xu et al., 2024 Han et al.] [002a]] Zhao et al., 2024a).. The ini-
tial attempts are deterministic methods focusing on spatiotemporal modeling. Researchers explore
different spatiotemporal modeling structures such as RNN (Shi et al.] 2015 Wang et al.,
2022
CNN (Gao et al., 2022a Tan al., 2023), and Transformers (Gao et al., [202bb] Liu et al.
2020
However, these methods have a shortage of blurry predictions, which hamper the nowcasting of ex-
treme events. Probabilistic methods are proposed alleviate blurriness (Ravuri al.) 2021 Gao
et al. 2024, /Zhao et al. 2024b Ji et al. 2022; 2023, Luo et al. (022).. DGMR (Ravuri et al. 2021) applies GAN to produce realistic and spatio-temporally consistent predictions to reduce blurriness. CLGAN J et al., 2022 2023) introduces an adversarial network with novel long short-term mem- ory to improve the nowcasting skills of heavy precipitation events. These GAN-based methods sig- nificantly increase the similarity between the predictions and ground truth, but the unstable training and inaccuracy of position and shape (Yu et al.] (024) hinder the further improvement on short- term forecasting. To further enhance precipitation nowcasting with accurate global movements, later methods combine blurry predictions with probabilistic models. Two-stage UA-GAN (Xu et al.) 2022), DiffCast (Yu et al., 2024). and CasCast (Gong al.) exploit how generate scall-scale weather pattern conditioning on the blurry predictions. Although these deterministic-probbbilis coupling methods achieve both global accuracy and local details, they are limited blurry predic- tions in the training of probabilistic parts. From the perspective of deblur, our method is proposed to train the probabilistic part without the requirement of blurry predictions.
2.2 IMAGE DEBLUR WITH DIFFUSION MODELS
Diffusion-based models have been widely investigated in image deblurring tasks since it is capable of generating high-quality clean images (Song & Ermon 2019 Ho et al. 2020 Song & Ermon 2020 Fei et al. 2023). As a pioneering work, U-Net architecture is trained in (Ho al. 2020) with a denoising objective
iteratively refine the generated image starting from pure Gaussian noise. For instance, Austin et al. (222)) introduced Discrete Denoising Diffusson Probabilistic Models (D3PMs) as a way to generalize the multinomial diffusion model by incorporating non-uniform transition probabilities. Diffusion models can be conditioned on class labels or blurry images to fur- ther enhance the performance of deblurring effects (Dhariwal Nichol, 10 Saharia et al. 2022a). Ren et al. (0023) proposes the icDPM, which can better understand the blur and recover the clean image with the blurry input and guidance from the latent space of a regression network. However, these methods merely use the blurry image as a form of guidance, rather than attempting to simu- late the blurriness itself, which renders them incapable of achieving a more precise and complete removal of the blur. Our model attempts simulate and update the blur kernel and guidance scale in
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Kernel
Kernel
Kernel
Kernel
Adding Guidance
Para-Update
Zt
Blurry y
20
Ko0
Adding Guidance
Parameter Update
Distance Metric [(KY (0o), 2/2
Reverse Process 27
Original
Image
Adaptive Guidance Scale
Zo Estimation
Forecast Model
V Li
Blurring Kernel K
Blurring Kip (To)
Forecast Result 1/'
Update Kernel
Gradlent Simulation
Pretrained DDPM
Distance Metric L(C, (5o),
Kernel Kt-1
Sample 3z.
Figure 2: Overview of PostCast for diffusion-basel precipitation predictions deblurring. (a) Unconditional diffusion model trained on datasets is used to eliminate noise and estimate To at every reverse step f, while an optimizable blur kernel is utilized simulate the blur contained in the blurry image y'. PostCast introduces a distance metric at each step of the reverse process to quantify the loss between the blurry image y' and the generated image žg after the blur kernel. (b) The Sampling process integrates pre-trained diffusion model with guidance from the distance function. The gradient could be employed for updating and simulating a more accurate blur kernel.
real-time by introducing the guidance of blurry predicted images form unconditional diffusion model. The simulation of blur makes our model suitable for the general dataset while generating clean precipitation images with rich and accurate details.
METHOD
In precipitation nowcasting, the increasing blurriness with lead time is crucial problem to be solved, as the blurriness impedes the accurate spatiotemporal modeling of small-scale weather pat- terns, which are related to most extreme precipitation events. Previous methods, utilizing the pairs of blurry predictions and observations to train models for the predictions of local weather patterns, challenged generalize well to blur modes that do not appear in training. Instead of directly predicting small-scale weather patterns by conditioning on historical observations and blurry predic- tions, we propose new pipeline composed of estimating the blurriness precipitation nowcasting directly and deblurring the blurry predictions with an unconditional diffusion model.
3.1 EXPLICITLY MODELING OF THE BLURRINESS IN PRECIPITATION NOWCASTING
There are many blur modes in blurry predictions precipitation. On the one hand, the blurriness in predictions varies depending the changes in lead time or fluctuations in weather conditions, influencing both the future probabilities and magnitudes of future changes. On the other hand, demonstrated by the visualizations in Appendix A.2 the differences in spatiotemporal modeling also have impacts on blurriness.
We first propose explicitly model these blur modes in precipitation nowcasting with unified formulation:
conp(KS....M
The above equation means that the blurry prediction y is recognized implementing convolution operation on the prediction y with local weather patterns similar to observations. K..TT. repre- sented by learnable matrix, is the kernel of convolution. The parameters of K.S.T.M vary

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according to sample (S), lead time (T), and prediction model (M), as blur modes are influenced by weather conditions, lead time, and spatiotemporal modeling.
3.2 UNSUPERVISED DEBLUR FOR ANY BLUR MODES IN PRECIPITATION NOWCASTING
Inspired by the formulation of Equation T fuzzy prediction could be tackled by solving the fuzzy inverse problem K.....M- However, in precipitation nowcasting, weather conditions vary with space and time, lead time changes in different application scenarios, and spatiotemporal modeling con- tinuously advances. As a result, it is prohibited to generalize to all blur modes in precipitation nowcasting by supervised training with pairs composed of blurry predictions and observations.
To cope with countless blur modes in precipitation nowcasting, proposed an unsupervised de- blurring method based on a pre-trained unconditional diffusion model. Specifically, there is a zero- shot blur estimation mechanism and auto-scale gradient guidance strategy to generalize our method to any blur modes in precipitation nowcasting.
3.2.1 ZERO-SHOP BLUR ESTIMATION MECHANISM
As shown in Figure our method adds guidance with the blur kernel Ks.T.M .M and blurry prediction y' in each reverse step of the pre-trained diffusion model. The parameter of K.S.T.M is randomly initialized and dynamically optimized at each step of the sampling process. In each reverse steps, there are two parts named "Adding Guidance" and "Parameter Update", respectively.
Adding Guidance, As shown in Figure during this process, the generated radar image So from pre-trained DDPM, which is calculated by estimating and eliminating the noise contained in 2,, un- dergoes convolution function with blur kernel 1 to establish distance metric L L(K, (mo), v') with blurry prediction y'. Detailed introduction of co can be found in the Appendix |A.4| Distance function quantifies the discrepancy between deblurred maps convolved with the blurry kernel and the blurry prediction map, aiming to generate outputs that are closer to y' after being subjected to the simulated blurry effect. And the guidance from the blurry prediction ensures the accuracy of the model's 's deblurring process through "Parameter Update", while the blur kernel connects the blurry prediction y and the generated radar image žu-
Parameter Update. As shown in Figure 2(b), after calculating the distance function for incorpo- rating guidance from blurry maps, the mean of xa is updated to sample 3T-1, while concurrently updating the convolution kernel parameters to more accurately simulate blurry in subsequent steps. To implement our zero-shot blur estimation mechanism, we employ V.L.p..g the gradients of distance metric L respect to kernel parameter ;Pz, to estimate the blur kernel from scratch by dynam- ically updating the parameter itself. Additionally, the distance metric L also provides the gradients respect to 2t, V,, E.p. which is utilized to guide the sampling of æt-1-
Specifically, the sampling process of the diffusion model transforms distribution po (= into conditional distribution Po Po v/). Previous work (Dhariwal & Nichol 2021 have derived the conditional transformation formula in the reverse process:
log 1 y/) = log (po(z, (ate//=+t))p(//11 (2,)(2,))))))))))))))))))))))) + +N
(2)
R log (2) + .2 2 ~~ N(2; (o + VV,, 2, log p(v' [=t))=:=p: EI),
(3)
where N₂ -og [x41), ,,,,) N2 is constant related the gradient term Vz, log p(y (m
And the variance of the reverse process Σ = (x₂) is set as a constant. Detailed derivation and proof are shown in the Appendix A.4 Based on this derivation, reverse process po (0 12 y/) integrates the gradient to update the mean (40 () generated from the pretrained DDPM. We exploit the gradient of distance metric to approximate the value of V., log p(y') (x)):
Vn, log p(y
- ((O). (To).
(4)
3.2.2 AUTO-SCALE GRADIENT GUIDANCE STRATEGY
Among them, is the scaling factor employed to control the degree of guidance and plays vital role in the quality of radar image generation. However, as there are numerous blur modes in precipitation
Xc represents the blur kemne Ks.T.M M with parameter yp step the reverse progress
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Algorithm Guided diffusion model with the guidance of blurry prediction y'. An unconditional diffusion model ea (Zee 1) fine-tuned on 5 datasets is given.
Input: Blurry prediction v' optimized blur kernel with parameters y,, learning rate I, guidance scale distance metric L.
Output: Deblurred prediction To conditioned on y'. Sample br from (00,
I: for all I from T to do
2: To vaa
55 L(y 1C, (To))
(#(-N) 7 g+C
4:
LiK (30).0')
5:
to + Zo s11-3e)
Vig Lijã
Vac-10e
1 To
6:
1-ôr
1-
7:
3. 1-8-1 Be
Sample Jt from N(1,, Bel)
9:
+ 9 - VV
10: end for
11:
return 150
nowcasting, it is difficult to set the guidance scale for each blurry mode. Instead, we propose an auto-scale gradient guidance strategy to adaptively derive s for any blurry prediction from an empirical formula:
18--10 -
(5)
where g refers to the Zz, log ply m 2,= and C log ply (100(=))22.=ppp
The detailed derivation
process of is shown in Appendix
A.4
The details of PostCast are shown Algorithm|] T PostCast undergoes reverse steps to gradually restore pure Gaussian noise steps t, mean μul (Zt: t) is integrated with Va, log p(y'|zi to sample øt-1- The blur kernel parameter which is related reverse step t is dynamically updated by the gradients of distance metric L.p. The optimizable blur kernel Nr and auto-scale guidance factor s enable the model to achieve blur simulation and flexibly eliminate blurriness for any blur modes in precipitation nowcasting.
(0, to high-quality precipitation images. For each reverse
EXPERIMENTS
This section includes the experiment setups and the analysis of the results. We begin with imple- mentation details in Section 4.1, and evaluation metrics in Scction 4.2 In Section 4.3 4.4 and
we present comprehensive experiments exhibiting the high generalization ability of PostCast to enhance the extreme part of predictions generated by classical spatiotemporal methods. Finally, the ablation study of PostCast and further analysis of the blur kernel K. ar and auto-scale guidance are presented Section 4.6
4.5
4.1 IMPLEMENTING DETAILS
We uniformly resize the radar images from all datasets to 256 x 256. Five datasets, including SEVIR (Veillette hai (Chen et al. 2020). and SRAD2018 (SRAD) 2018), are selected to train the unconditional DDPM, while the other datasets (SCWDS CAP30 Na et al. 2021), SCWDS CR (Na et al. 2021), MeteoNet (Larvor & Berthomier 2021)) are prepared for nut-of-distributt
2020). HKO7 (Shi et al.) (017). TAASRAD19 (Franch al. 2020). Shang-
testing to evaluate the generalization of each method. More details each dataset can be found in Appendix A.6 We fol- low (Dhariwal & Nichol 2021) to train our DDPM. We utilize the pre-trained unconditional diffu- sion model on ImageNet for better initialization and fine-tune it on SEVIR. HK07, TAARSARD19, Shanghai, and SRAD2018 using AdamW with 31 0.9 and Bg 0.999. PostCast uses blur kernel with a size of 9. To recover the prediction with distribution real observation, we implement method with 1000-step DDPM. The cosine learning rate policy is used with initial

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Observations
Blurry predictions
+PostCast
Figure 3: Visualization of applying our PostCast on 5 datasets at time step 12 when the spatiotem- poral prediction model TAU.
Table 1: The CSI scores of the highest thresholds evaluated by P1 (max pooling 1), P4 (max pooling 4), and P16 (max pooling 16) at time step 12 (about hour lead time), as well as HSS and POD.
Model
SUIII
18807
THASSADDI
Shangton
SHAIZSOIK
24
1551
1001
P1
P16
1558
POD1
P4
P15
HS51
2001
P1
P4
716
1551
POD1
P16
PODT
B1055 6404
0.165
0.402
01485
09090
11327
0.220
0.386
0.589
1.046
0.022
0.127
0.311
0088)
0.216
10388
0.000
0.156
0.ER0
0.538
PredRNN
10013
0014
0,017
0.375
0.388
0.006
8008
0.013
0.352
0.364
1.006
0,00
0012
0.237
0.178
0.00
D012
0.00
0.268
0.21
0.025
0.444
0051
0.209
(.221
1883
0.422
0.305
0.004
0.01
0.39
0.256
SmmV
11915
0.024
0.389
0,385
00942
08449
0107
0.409
0.338
11000
12000)
0/002
0.242
0.181
00230
0.091
1130
0.236
0.099
01641
0.53
B045
0389
01340
0.409
11462
01154
16316
0.264
0.385
0.409
[1:21
03055
01811
0.225
0.215
03844
B0994
01212
11305
0.300
0.009
0.172
0.272
0.301
1.300
EailFFance
0.021
0.021
0/040
DiCCci
8009 6.00 0.196
3.361
0.378
(090)
11315
03265
0.385
0.374
(8086
0.066
0.174
0
0.761
(.09)
D/997
01:18
11300
0.787
0.011
0:134
0.117
0.31
0:307
8:59
0.67
0.111
01637
(1054
11108
01:11
0.178
107
11749
0.135
0,50
STRPM
[1007
6,111
0.200
00000
0.074
0.265
0.016
0.054
0/478
01216
[0000 0342 0010 03372 0,38 (002N I0B8
0.187 0,312 0.323
[LOIK 0344 0/994 0.236
0.196
0112
D000 0.100 IL382
0.271
00044
0189
0,1/6
0.205 00230
learning rates 0.0002 for PostCast and the Br we utilize undergoes linear increase from 31 10-4 to 3T = 0.02. 4.2 EVALUATION METRIC
We choose the Critical Success Index, Probability of Detection (POD), and Heidke Skill Score (HSS) for evaluation. For each dataset, the thresholds with the highest intensity are selected to quantitatively evaluate the accuracy of predictions for extreme events by CSI. The blurriness in de- terministic predictions influences the modeling of small-scale patterns, which are usually correlated to extreme precipitation events. Meanwhile, average CSI, POD, and HSS scores across different thresholds are evaluated to provide a more comprehensive evaluation of precipitation with different intensities. Details of evaluated threshold scores, we set the predicted and observed pixel values less than 7 to 0 otherwise 1. These binary val- ues enable us to determine the true positive (TP), false negative (FN), true negative (TN), and false positive (FP) counts. The formulations are: CSI = TP4FN+PP. POD TP-FN. HSS = PXIPPXNNPNXXP
given in Appendix. A.5 Before calculating these
TP
CSI and POD values vary between 0 and 1, while HSS value varies between -1 and 1, with values approaching 1 indicating higher level of agreement between the predicted and observed results.
4.3 EVALUATION ON MULTIPLE DATASETS
Table I presents the quantitive evaluation results of our method's performance gain for extreme pre- cipitation noweasting and general predictions. Specifically, TAU (Tan et al. 2023). PredRNN (Wang et al. 2022). SP (Gao et al. 2022a), and EarthFormer (Gao et al., 2022b) are all indepen- dently trained on the evaluated datasets. We compare method with four supervised GAN- or

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Observations
Blurry predictions
+aostCast
Observations
Bluryy predictions
PostCast
30min
660mnn
60min
Figure 4: Left: Visualizations SEVIR. Right: Visualizations on HKO7. Both blurry predictions are given by model TAU. The lead times of predictions are 30 minutes, 60 minutes, and 90 minutes. Table 2: The CSI scores of the highest thresholds evaluated by P16 at different lead times.
Model
HK07
SEVIR
Model
HK07
SEVIR
M0nin
90min
30min
60min
90nin
M0nin
SumVP
tours
0.309
0.228
0.144
10urs
0.252
Diffusion-basee methods which are jointly trained on these five datasets (DiffCast YYu et al. 2024), CasCast (Gong al.), DGMR (Ravuri al. (02T), STRPM (Chang et al., 2022g), and an unsu- pervised GAN-based method (DGP (Pan et al. 202T)). Our unconditional DDPM is also jointly on the five datasets in Table T which makes this evaluation in-domain. As shown in Table [ our method can be applied all of these prediction methods including RNN-based. CNN-based, and Transformer- based. It demonstrates that our method is not sensitive to the way how the spatiotem- poral correlations precipitation evaluation when applying our method, which is attributed to the local weather patterns recovered by our method as exhibited in Figure 3 Besides, reveals the potential of method to adapt to different blur modes in precipitation nowcasting related to datasets. For general prediction quality evaluated by avg-HSS and avg-POD scores, it is significantly improved after applying our PostCast. In summary, PostCast demonstrates outstanding improvement across different prediction models and datasets, proving its effectiveness and flexibility recovering local weather patterns and increasing the general precipitation nowcasting.
modeled. On each dataset, there are significant improvements in extreme
4.4 EVALUATION AT ANY LEAD TIME
Encouraged by the generality PostCast among datasets and models, this section, we exhibit the ability of PostCast to be generalized to arbitrary lead times such as 30 min, 60 min, and 90 min, within a zero-shot manner. An example is visualized in Figure showing our method enhances local details and boosts the predictions of extreme values. As shown in Table we conduct experiments on SEVIR and HKO7. For both datasets, no matter which spatiotemporal prediction models are used, our method consistently increases the CSI scores of the highest thresholds (32.24 kg/ /m2 for SEVIR, and 30 mm/h for HK07). Specifically, in SEVIR, the highest CSI scores of 30 min, 60 min, and 90 min reach 0.252, 0.144, and 0. 0.106, respectively. The highest CSI scores evaluated on HK07 reach 0.394, 0.326, and 0.266 for the lead time of 30 min, 60 min, and 90 min. The consistent gain indicates the generality of our method among different lead times.
kg/m²
4.5 DEBLURRING ON OUT-OF-DISTTRIUTTIO DATASETS
We compare PostCast with other methods described in Section 4.3 on three datasets. The quantitative results are presented in Table 3 CAP30 and CR represent different modalities (constant altitude plan of 3 km and composite reflectivity) in SCWDS. These datasets
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Table 3: Evaluation on out-of-distribution datasets. The CSI scores are calculated within the highest thresholds at a lead time of hour. PI, P4, and P16 indicate max pooling 1, max pooling 4, and max pooling 16, respectively,
Model
SCWIS CAP30
SCWDS CR
MeweoNe
P.66
1555
0.022
0.240
0.067
0.224
+1
0.025
0.000
0.106
0.157
0.235
0.554
0/966
0.033
0.065
0.875
0.136
0.209
0.345
0.428
0.143
0.214
0.338
0.444
0.549
0.024
0.059
0.862
0.288
0.341
PredRNN
0.088
0.044
0.008
0.239
0.203
0.940
0.043
0.066
0.351
0.323
0.000
0.00
0.00
0.230
0,190
-CuCaa
0.35
0.06
0.123
0.253
0231
0036
0.13M
0.281
0.337
0.900
0.30
0.201
0289
0.23
DbllClal
0.07
0.85
0.936
0.01
0.76
0.215
0.326
SinVP
0,025
0.245
0.001
0.111
0.238
0.314
0.119
009
01030
0,853
0.34
0.281
0.315
+DdCCial
0.024
0.844
0.129
0.224
0.201
0.047
0.071
0.169
0.205
0.290
0.017
0.037
0.105
0.240
0.215
-00r
0.883
0.156
0.255
0.567
0.416
0.140
0.205
0.2%6
0,489
0.545
R1825
0.854
0.147
0,316
0,91
Burtherree
0.011
0,044
0.056
0.24
0.55
0102
0.065
0.061
0.417
0.405
0,000
0.006
0/001
0.251
0.11
-CaCrd
0.850
0.080
0.150
0.055
0.159
0.366
0.265
0,011
0.011
0.175
DGMK
0.088
0,088
0.100
01161
0.155
0.099
0,057
0.192
0,123
0.131
STRPM
0.014
0.499
0.
0.234
0.197
0.029
0.060
0.301
0.296
0.364
0.014
0.946
0.145
0.192
0.155
DGP
0.027
0.089
0.111
04.244
0.258
0.071
0.083
0.090
0.409
0.597
0.037
0.062
0.186
0.250
0.216
Table 4: The ablation study on the optimizable convolutional kernel and the adaptive guidance scale.
The CSI scores with the highest thresholds are calculated by P1 (max pooling 1), P4 (max pooling 4), and P16 (max pooling 16). The lead time is 60min for SEVIR and 72min for HKO7 (both 12 steps). In cells marked with a "V", the corresponding module is employed, while cells with " "X" indicate fixed guidance scale of 3500 consistent with the initial value setting of PostCast, and remain unchanged throughout each step of the reverse process. The reason for these settings can be found in Appendix A.7
random blur kernel with a mean value of 0.6, which is
Methods
Dynamic Update
SEVIR
HK07
Kemee
Guidance Scale
P1
P4
P16
P1
P4
P16
Model A
X
0.010
0.019
0.448
0.030
0.ss
0.219
Model C
/
X
0,038
0.064
0.115
0.059
0,102
0.232
PostCast
/
0.045
0.070
0.131
0.066
0.125
excluded from both the fine-tuning of our unconditional DDPM. Our method notably improves the nowcasting of extreme precipitation and general precipitation nowcasting even in the out-of- distribution datasets. Specifically, on SCWDS CAP30, the highest P16, HSS, and POD of our method reaches 0.269, 0.367, and 0.436, respectively, while other methods only achieve 0.226, 0.314, and 0.3 On MeteoNet, PostCast fulfills the best HSS and POD across all methods. More- over, our method achieves competitive performance when applied on Si and EarthFormer, while on TAU and PredRNN, our method remarkably enhances the CSI scores, reaching 0.182 on TAU and 048 on PredRNN. Such gap in performance on different spatiotemporal prediction models further reveals the requirement for generalization to various spatiotemporal modeling approaches. To summarize, our method impressively enhances the blurry predictions even on out-of-distributtin datasets.
4.6 ABLATION STUDY
In this section, we conduct ablation to validate the effectiveness of our proposed zero-shot blur estimation mechanism and auto-scale gradient guidance strategy. Additionally, further analysis of the blur kernel estimation and the auto-scale guidance is conducted.
Table presents the results of the ablation study on SEVIR and HK07. "Kernel" stands for the zero-shot blur estimation mechanism, and "Guidance Scale" represents the auto-scale gradient guidance strategy. shown in Table[[] when the deblur progress is equipped with neither "Kernel" nor "Guidance Scale", it exhibits relatively low CSI scores for extreme precipitation nowcasting- When "Kernel" is solely applied, there are significant gains on both SEVIR and HK07, especially on the CSI-PI evaluated pixel-wisely, In particular, the CSI-PI of SEVIR reached 0.038 and that of HK07 reached 0.059, indicating the importance of estimating the blur modes. Further, "Guidance Scale" improves the gain of "Kernel", which suggests adaptively scaling the guidance contributes to better guidance.
Further experiments are conducted to reveal the influence of "Kerne"" and "Guidance Scale". As shown in Figure (a), for the SEVIR dataset, the mean of the kernel stabilizes around 2.65 reverse
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(b)
Menn llterry Kerm!
0.30
0.15
7.3
0.65
14
Minimum Value
Average Value
Maximum Value
8-5
Numerical Range the Sevir Dutsnet
2
2.30
Time Steps
Figure 5: (a) The distribution of the mean of blur kernel at reverse step 0 on SEVIR dataset. (b) The variation the mean of the blur kernel with reverse steps on SEVIR dataset.
Model Output
GT
Model Output
GT
200
300
(9)
(b)
Figure 6: (a) Variations in the guidance scale can result in different intensities of precipitation. From left to right, the guidance scale values are 1.25, 1, and 0.75 times our auto-scale gradient guidance strategy; (b) Different initial values of the blur kernel parameters affect the resultant precipitation intensity maps to varying degrees. As the initial values of parameters decrease from left to right, the precipitation intensities correspondingly diminish. The intermediate map in the model output represents the model's standard parameter settings, and the results are closest to the ground truth.
step t = 0. And for single map from the SEVIR datasets, as illustrated Figure (b). the mean of optimizable blur kernel parameters increases with the sampling process. This increase in magnitude influenced by the gradient of the distance metric with respect the parameter. Ultimately, the mean value of convolutional kernel parameters gradually converges to approximately 2.65.
The visualization of Figure 10 indicates a close resemblance between the PostCast output map con- volved with the optimizable blur kernel at time f = 0 and the prediction map, suggesting that the blur kernel effectively estimates the blur present within the prediction map, allowing the model to generate high-quality outcomes with faithfulness details that are similar to the ground truth.
Furthermore, the "Kernel" and "Guidance Scale" are capable of controlling the intensity of gener- ated precipitation predictions through parameter adjustments. As illustrated in the zero-shot blur estimation mechanism, combined with auto-scale gradient guidance strategy, enables the model to produce results that most closely approximate the ground truth in terms of small-scale structures and precipitation intensity. By manually increasing or decreasing the values of the guidance seale and blur kernel, we can either enhance diminish the precipitation intensity in the generated im- ages. This outcome demonstrates the model's substantial controllability, allowing it to meet diverse usage requirements with greater flexibility.
5 CONCLUSION
In this paper, we propose PostCast, a generalizable postprocessing method for precipitation now- casting to enhance the local weather patterns and extreme precipitation nowcasting. Specifically, integrates the generative prior in the pre-trained diffusion model with zero-shot blur kernel es- timation and auto-scale denoise guidance enhance blurry predictions. Experiments demonstrate that our method could increase the ability of extreme nowcasting for varying datasets, different lead times, and multiple spatiotemporal prediction models.
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ACKNOWLEDGEMENTS
This work is Supported by Shanghai Artificial Intelligence Laboratory. This work was done during Junchao Gong's internship at Shanghai Artificial Intelligence Laboratory.
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A APPENDIX
A.1 LIMITATIONS AND FUTURE WORK
Our work has certain limitation: As postprocessing method, the effectiveness of PostCast par- tially depends on the spatiotemporal prediction models. This may lead to poor performance the blurry predictions have relatively low accuracy. Motivated by the importance of an accurate pre- diction, in the future, we will make an effort to improve our method with priors of spatiotemporal prediction modeling.
A.2 VARING BLUR MODES
In this section, we visualize the blurry predictions with different blur modes. As shown in Figure 7 TAU has a sharper prediction than other models, which indicates the blur modes vary with the spatiotemporal modeling approaches. We also demonstrate that the blur modes change with the lead time in Figure Longer lead time results in a stronger blurry mode and weaker extreme predictions. Figure presents the different blur modes across datasets samples. In this case, the samples of
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Figure 7: Visualization of predictions of different spatiotemporal prediction models at the lead time of hour MeteoNet.
+t0m
+30mh
+00mm
Figure 8: Visualization of predictions EarthFormer on SEVIR at different lead times.
SEVIR and TAASRAD19 suffer from severe blurry mode compared, while those of MeteoNet and HKO7 have more local weather patterns. In summary, the blur modes vary with spatiotemporal modeling approaches, lead times, and weather conditions which represented by datasets and samples. There is an urgent requirement for method to deal with the countless blur modes.
A.3 PRELIMINARY
Diffusion model is a generative framework that encompasses both the forward and reverse pro- cesses. The forward process gradually destroys the original training data vo by adding Gaussian noise successively, which is defined as Markov chain:
g(<1 "e2 = II q((r(e=-1).
(6)
Pure Gaussian noise can be obtained after T diffusion steps when large enough. Each diffusion steps is defined by the given parameter series 3r, which refers to the variance of the forward process. can be set as a known constant or learned with separate neural network head (Nichol & Dhariwal 2021
( = Naz;; / 8,2,-1,
The reverse process is the inversion of the forward process, aiming simulate noise from the noise distribution at each diffusion step and recover data from it. However, the mean and variance of the reverse process conditional distribution (o(xx-1lzz) late directly. Therefore, we need to learn noise simulation function en (xr, t) parameterized by parameter 0 to approximate the mean of the conditional probabilities, which enables the model to simulate and eliminate noise in the data sampled from the reverse process.
(I ((2,, t), E/) is difficult to calcu-
Bayesian formula can be utilized transform the conditional distribution follows: Bayesian formulas can be employed to derive the mean and variance for each step in the reverse process Po(#s-1(zz).
g(xx- (ZO) g(<z|2o)
g( 12,, vo) (0
(8)
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Figure Visualization of I hour's prediction EarthFormer on different datasets.
Directly expanding the three terms at the right- of Equation g the mean js and variance Eg can be represented by the following equation:
1o( 1
(x+ VI- a
()()))))
V0t
Eo(r1)= 1 01-1 31
(10)
Ciy
where O₁ = 1 - Be and ã [ 0t,, which indicates that the variance of the reverse process = Eg(zz) is a constant.
Diffusion model utilizes maximum likelihood estimation to obtain the probability distribution of Markov transition the reverse process. Specifically, the noise prediction function eg cx 1) trained with the purpose of optimizing the following surrogate denoising objective.
E. |||e co (z,, )
(11)
A.4 EMPIRICAL FORMULA OF GUIDANCE SCALE
In the reverse process the diffusion model, we incorporated guidance from y/ to transform the initial reverse denoising distribution po(x, [(+1) into conditional distribution po (æx #e+1,
Proven by (Dhariwal & Nichol 2021), this distribution can be further simplified:
po(as, Te+1. y)
Po( 2141.8)
(12)
po( y)
po (2,, F1+1,
(13)
Po(y
Fe+1) 1P
po(ar
me+ 1++1)po( 24, 11)
(14)
po(y) 1=41)po(<(44
Po(y
[e Fe41 (1441)pp(2 xe+1)
(15)
Po x2+1)
po(y (p (are w241)
(16)
py xe41)
ply we (p (441)
(17)
Pe (y x++1)
Distribution Po(y] ( is independent of Ir, so we use the constant N instead:
po xe+1, y) = (r de+1 1++1)po(y xx)
(18)
Taking the logarithm of both sides of the equation, it can be obtained that:
log po (a 2 y) log (po(2a) N..
(19)
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normalizing constant. For the first term, the posterior (a [(+1) used for sampling is hard to calculate directly. Therefore. we utilize the model with parameter 0 pre-trained on five datasets to approximate the conditional probabilities.
where N1 -log N can be
Consider the expansion of po (++1:
log po (2 (oo (v) log Po (3r (1e+1) + log (o(()))))
(20)
2 (o+ nO) En (xt po) + log po (v( + C1
(21)
Cj is constant generated from the expansion of pa (1 where
C = log (2T) ([>ie) b) = log 2
log |>e
(22)
Term log po (0(( reflects the guidance from blurry map y integrated in the reverse process of sam- pling Tt-1- The introduction of distance metrie L and guidance scale is exploited to characterize the value of log Po(y)zz as a heuristic algorithms:
log po(y) (()) v),
(23)
where To is calculated by estimating and eliminating the noise contained in æ;;
v1 (x1, t)
30
(24)
V 0z
Meanwhile, taylor expansion around 2; = po can be leveraged to estimate log (o(u)))). By taking the first two terms of the Taylor expansion, can be estimated that:
log po Po (ax) log po(y) == + (ate (O) VVa log po (yl
(25)
= C2 (ra -
/o) Vz, log ( x 1=
(26)
By combining the heuristic approximation formula and Taylor expansion mentioned above, can derive the empirical formula for the guidance scale:
(xe 1)) as log Pa (y) + C2
(27)
C(K) (20), v)
Among them, f is obtained by pre-trained DDPM. C2 is minor constant that can be disregarded. By incorporating Equation 26 we can further simplify Equation 2T (Taking g replace the gradient
Va, log (o (=:
log (o( (v) =e
(z, uo0 " (Ie - (D0 + (xt (o) + C1+
(28)
2
1
(Et ne >T 2i (xt pe >Bgg) 79 Deg + C1 C2
(29)
log p(2) + N₂, 2~ N Eag, Eo),
(30)
where N₂ = - Eog + C2 is constant related to g. Equation 30 is utilized sample xt-1 every
reverse process t.
A.5 DETAILS OF EVALUATION THRESHOLDS
Specifically, the evaluated threshold 7 for these datasets is
32.24
kg/m²
for SEVIR
30
mm/h
for HKO7, TAASRAD19, SRAD2018
40
dbz
for SCWDS CAP30 and SCWDS CR
47
dbz
for MeteoNet
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For the SEVIR dataset, convert the VIL pixel into R with the units of kg/m², which are the true units of VIL images, by the following rule (Viileetee et al. 2020):
0, x
if x 5
R(x)=
5 S 18
60
exp(
83.9
18<2 S 254
38.9
Additionally, we transform the radar reflectivity values with dBZ unit in HK07, TAASRAD19 and SRAD2018 into rainfall intensity values (mm//h using the Z.-R relationship (Shi et al. 2017 Franch et al. 2020):
dBZ = 10 lollogo a + 10b log I
(31)
where R is the rain-rate level, a 58.53 and 1.56.
A.6 DATASET DESCRIPTIONS
SEVIR Veiilette et al. 2020) We use the data of NEXRAD which are processed into radar mosaic of Vertically Integrated Liquid (VIL) in SEVIR. It contains image sequences for over 10,000 weather events that cover 384 km 384 km patches. These weather events are captured over the contiguous (CONUS). The VIL image has a spatial resolution of 384 384. We use weather events in 2017 and 2018 for training and events in 2019 for validation and testing.
HK07 Shi et al. 2017) In HK07, the radar echoes are CAPPI (constant altitude plan position indicator) images covering 512 km2 region centered on Hong Kong, captured from an altitude of 2 km above sea level. The spatial resolution of data is 480x480. HKO7 contains observations from 2009 to 2015, Observations in the years 2009-2014 are split for training and validation, and data from 2015 are used for testing.
TAASRAD19 (Shi et al., 2017) It collected radar echo images from 2010 to 2019 for a total of 1258 days using the maximum reflectivity of vertical, by the weather radar of the Trentino South Tyrol Region, the Italian Alps. The images cover an area of 240 km with a spatial resolution of 480 4880. The radar echoes from 2010-2018 are split into training part, and those of 2019 are used for validation and testing.
Shanghai (Chen et al. 2020) it dataset that contains radar echoes represented by the CR (com- posite reflectivity) collected from the dual polarization Doppler radar located in Pudong, Shanghai. The raw data, spanning from October 2015 July 2018, are interpolated to 0.01 x0.01 longitude- latitude grids with a spatial resolution of 501 x 501. We use data from 2015 to 2017 for training, and
2018 for validation and testing.
SRAD2018 (SRAD 2018) This dataset covers Guangdong Province and Hong Kong during the flood season from 2010 to 2017. The size of radar images is 501 x 501 with a spatial resolution of km. We follow (RRD 2018) to split the dataset for training, validation, and testing.
MeteoNet is a dataset covering two geographical zones, North- West and South-Fast of France, during three years, 2016 to 2018. The radar MeteoNet has a spatial resolution of 0.01", We crop the data to keep the top-left portion with size of 400 400.. We train the models with images from 2016 2017 and validate or test the models with images of 2018.
MeteoNet (Larvor & Berthomier] 2021)
SCWDS (Na al., 2021) It is released by the National Meteorological Information Center of China, including total of 9832 single-station strong precipitation processes across Jiangxi, Hubei, Anhui, Zhejiang, and Fujian from 2016 to 2018 We select 3km-CAPPI images as the modality of the SCWDS dataset. Each radar image in SCWDS covers 301 grids with resolution of 0.01 x 0.01. Images from 2016 and 2017 are used for training, and those from 2018 are processed for validation and testing.
For each dataset, we randomly sample 500 sequences from the test split to evaluate the effectiveness of our proposed PostCast and other methods.
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A.7 IMPLEMENTING DETAILS OF ABLATION STUDY
The ablation study regarding the effectiveness of proposed zero-shot blur estimation mechanism and auto-scale gradient guidance strategy is conducted at the predictions EarthFormer on HKO7 and SEVIR datasets, We utilize guidance scale 3500 for the fixed guidance scale model, as this setting closely aligns with the initial value the guidance scale calculated by the auto-scale gradient guidance strategy for HKO7 and SEVIR datasets. Concurrently, the size of the blur kernel utilized in the ablation study is 9 x 9, and its initial value in the fixed-kernel model is randomly drawn from Gaussian distribution with mean of 0.6. This configuration is identical to that of the kernel's 's initial value PostCast, thereby ensuring a fair comparison can be conducted Table
A.8 RECOVERING THE BLURRY PREDICTIONS BY APPLYING BLUR KERNELS
Output
Blur
Pred
Output
Blur
Pred
100
150
200
250
50
100
150
200
250
Figure 10: Visualization of a one-hour prediction by EarthFormer on the SEVIR dataset. From left to right, the three maps represent the PostCast output, output convolved with the optimizable blur kernel at time f = 0, and the prediction map. The convolved map closely resembles the prediction map, indicating that the model's blur kernel is effective in constructing and simulating the blur in the prediction map.
In this section, we validate whether the optimizable blur kernel effectively emulates the blur present in the prediction map by examining the correlation between the PostCast output and the derived blur kernel. The optimizable blur kernel is proposed to dynamically adjust its parameters in real-time during the reverse steps simulate blur. As illustrated in Figure TO the simulated blur map obtained by feeding the PostCast output after T steps into the blur kernel exhibits no significant discrepancies with the actual prediction map. This indicates the efficacy of the blur kernel's 's parameters in faithfully replicating the blur inherent within the prediction map.
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Table 5: The quantitive comparisons average CSI scores, SSIM, and PSNR.
Model
SEVIR
HX007
TAASRADI9
Shuughai
SRAD2015
CSIaagg
SSIM1
PSNR1
CSi-aeg
CSlerg.
SSIM:
FSNR
CSlarg
SSIM
PSNRT
CSlaagg
SSIMT
PSNR1
21.52
0.305
0.648
0.822
20.66
25.27
0.750
20.00
0231
0.750
20,00
0.383
0.675
16.05
0.183
11.3
0.081
27.21
0.659
19.79
SinVV
0.301
0.611
22.83
0.30
0.665
21.18
0.157
0.397
27.46
0.302
0.753
30.71
6.11
0.73
23.55
0.312
0.645
22.18
0.63
0.80
20.30
0.193
0.710
25.61
0.202
0.758
19.74
02002
0.758
19.74
hanhFurmrr
0.783
0.673
22.79
0.270
0.61
21.55
0.100
0.793
26.59
0,315
0.772
11.09
0.001
0.009
27.21
+uum
0.303
0.65
21.73
0.270
0.666
30.65
0.182
0.713
25.31
0.119
01700
18.57
0.317
0.769
18.50
DitCCat
0.270
0.02
20.27
0.261
0.544
15.90
0.187
25.15
0.305
0.078
19.14
0.198
0.027
21.00
Cod'ae
0.991
0.80
13:02
0.116
33.79
0.121
0.25%
0.167
0.088
0.012
22.25
0.158
0.763
20.50
0.156
0,812
26.42
A.9 MORE QUANTITATIVE RESULTS
More quantitative results are presented in Table settings which are the same as those in Section After applying our PostCast, the CSI-avg. score increases SEVIR, TAASRADI9, Shanghai. and SRAD2018, while slightly decrease in HK07. It indicates that our PostCast boosts the accuracy of precipitation nowcasting in most cases. Image quality scores SSIM and PSNR are also computed. However, the PSNR and SSIM scores often penalize synthetic high-frequency details demonstrated in Google SR3 (Saharia et al. 2022b), such as for the weatleweather systems in weather prediction, the shape and position of local patterns may slightly deviate from the ground truth which may also result in inferior PSNR and SSIM scores but could be tolerated in applications. Moreover, these scores are not strongly related to nowcasting skills evaluated by the CSI. As postprocessing method of precipitation nowcasting, our PostCast mainly focuses on improving meteorological metrics such as CSI and HSS.
All the metrics are evaluated with in-domain
A. 10
MORE VISUALIZATION RESULTS
We provide more visualizations in Figure
Nanghri
Observatoon
Blurry predictions
*PostCast
Figure 11: Visualization of applying our PostCast on 5 datasets at time step 12 when the spatiotem- poral prediction model is PredRNN.
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Observations
predictions
PoostCast
Figure 12: Visualization of applying PostCast on 5 datasets at time step 12 when the spatiotem- poral prediction model SimVP.
SPAR
Shaghal
Observations
Blurry predictions
PostCCat
Figure 13: Visualization of applying PostCast on 5 datasets at time step 12 when the spatiotem- poral prediction model EarthFormer.
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Published as a conference paper at ICLR 2025
Observations
Blurry predictions
+PostCast
DiffCast
+astasst
lc]
Figure 14: Visualization of applying PostCast the out-of-distribution datasets at time step 12 when the spatiotemporal prediction model is TAU. (a): SCWDS CAP30. (b): SCWDS CR. (c): MeteoNet.
Doservations
Blurry
+DiffCast
(a)
Figure 15: Visualization of applying our PostCast on the out-of-distribution datasets at time step 12
when the spatiotemporal prediction model is PredRNN, (a): SCWDS CAP30. (b): SCWDS CR. (c): MeteoNet.
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Observations
Blarry predictions
+oostCast
DiffCast
+astasst
[c]
Figure 16: Visualization of applying PostCast the out-of-distribution datasets at time step 12 when the spatiotemporal prediction model Sim VP. (a): SCWDS CAP30. (b): SCWDS CR. (c): MeteoNet.
Doservations
Blurry
+DiffCast
(a)
Figure 17: Visualization of applying our PostCast on the out-of out-of-distribution datasets at time step 12 when the spatiotemporal prediction model is EarthFormer. (a): SCWDS CAP30. (b): SCWDS CR. (c): MeteoNet..
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Reviews:
[{"review_id": "OQaq0JGa4M", "reviewer": "Reviewer_SepJ", "summary": "The paper proposed a universal post-processing method, PostCast, designed for precipitation nowcasting. This method achieves denoising of convolution based prediction models using unconditinal DDPM through two key innovative points: zero shot blur estimation mechanism and auto scale gradient guidance strategy. The model was trained on a combination of five different datasets and prediction results generated by different models, and outperforms other conditional denoising models in terms of CSI metrics.", "questions": "The questions here are related to the three points described as weaknesses.", "limitations": "", "rating": 3, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper proposes a novel approach to estimating blurriness by utilizing the gradient of metric respect to kernel parameters, which serves as a guide for the sampling of x_ {t-1}.\n2. The paper introduces an auto-scale gradient guidance strategy that automatically calculates the guidance scale corresponding to different forecast time periods, models, and datasets. This strategy enables the model to adaptively denoise the prediction results.", "weaknesses": "1. CasCast and DiffCast are both designed for **predicting precipitation**, but not **postprocessing tasks**. It is crucial for the authors to compare with other generative models such as GAN. This would provide a more comprehensive evaluation of PostCast's performance against a wider range of methodologies.\n2. The authors have compared PostCast with CasCast and DiffCast only in **out-of-distribution datasets** but did not compare them on HKO7, SEVIR, etc.\n3. Relying on CSI as the only metric may not sufficiently judge the quality of precipitation prediction. The average intensity of the precipitation predictions can significantly impact CSI. For instance, using simple histogram matching on predictions can also significantly improve CSI. The authors should clarify whether the CSI improvement is due to **increased intensity** from the model or other factors to provide a more accurate assessment of the model's performance.", "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": "", "extra_scores": {}}, {"review_id": "qD7aTuG5TL", "reviewer": "Reviewer_iPfR", "summary": "This paper presents a novel postprocessing approach for precipitation nowcasting, named PostCast. The core innovation of PostCast lies in utilizing an unconditional diffusion model (DDPM) to remove blurriness from precipitation predictions without requiring paired data of real observations and blurry predictions. The method introduces a zero-shot blur kernel estimation mechanism and an auto-scale denoise guidance strategy, enabling the model to adapt to various datasets, prediction lead times, and blur modes to generate sharper predictions. The authors conducted experiments on multiple precipitation datasets and forecast models, demonstrating PostCast’s effectiveness in improving prediction accuracy for extreme precipitation events. The paper proposes a highly generalizable and adaptive postprocessing framework that shows strong potential for broad applications in precipitation nowcasting tasks.", "questions": "1. **Evaluation Metrics**: Could you add results for POD, FAR, and image quality metrics like PSNR and SSIM in the appendix to provide a more comprehensive evaluation of PostCast?\n\n2. **Related Work**: Could you expand the related work section to discuss GAN and Transformer methods in precipitation nowcasting to better contextualize the background and contributions of PostCast?\n\n3. **Comparison with GAN Methods**: Could you include comparisons with GAN-based methods in precipitation nowcasting to further validate PostCast's effectiveness?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. **Originality**: The primary innovation of PostCast lies in employing an unconditional diffusion model (DDPM) to remove blurriness from precipitation predictions without relying on paired data. By introducing zero-shot blur kernel estimation and an auto-scale denoise guidance strategy, PostCast adapts to various datasets, prediction lead times, and blur modes across different models. This unsupervised deblurring approach appears to be the first of its kind in the domain of precipitation nowcasting, demonstrating creative thinking.\n\n2. **Completeness of Experiments**: The paper provides a well-designed experimental setup, covering multiple datasets and forecast models to showcase the generalizability and robustness of PostCast. The experiments include diverse evaluation metrics and extensive visual results, effectively validating the method’s performance in extreme precipitation event prediction.", "weaknesses": "1. **Incomplete Evaluation Metrics**: The paper relies only on CSI, lacking POD, FAR, and similar precipitation metrics, as well as image quality metrics like PSNR and SSIM. Including these in the appendix would strengthen the evaluation’s comprehensiveness.\n\n2. **Insufficient Related Work Discussion**: The discussion of GAN methods in precipitation nowcasting is limited, and the introduction to Transformer-based approaches is also lacking. Expanding these sections would better contextualize PostCast’s contributions.\n\n3. **Lack of Comparison with GAN Methods**: There is no direct comparison with GAN-based approaches in precipitation nowcasting. Adding such comparisons would provide a more complete assessment of PostCast’s performance.", "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": "", "extra_scores": {}}, {"review_id": "mbe8Ho6GAq", "reviewer": "Reviewer_CFdX", "summary": "The paper proposes an unsupervised postprocessing method for precipitation nowcasting outputs to remove blurriness from these predictions, especially at longer lead times. This can be important to obtain accurate extreme precipitation events. The blurriness is eliminated with an unconditioned denoising diffusion probabilistic model guided by blurry predictions. The authors introduce a zero-shot kernel estimation method and an auto-scale denoise guidance strategy and show that their proposed framework generalizes to many blurriness modes across different datasets and varying lead times.", "questions": "The current metric/score mainly evaluates whether the small-scale features are recovered and extreme events are captured well after deblurring. However, isn’t it also important to assess how closely the nowcasting outputs, after deblurring with this methodology, match the ground truth, generally? If we don’t threshold the pixel values in this case and compute an error metric such as MSE and PSNR, that could also be informative. The authors can consider adding these scores.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The problem is well-motivated. An unsupervised method alleviates the need for labels and the model is no longer limited to the blur modes seen during training. Given that this framework is independent of the spatiotemporal prediction model and can be added on top of any prediction model, it makes this work very useful and widely applicable. \n- The results strongly demonstrate the ability of their proposed method. The experiments are extensive and prove PostCast’s effectiveness in recovering weather patterns across multiple prediction models, datasets, and lead times.", "weaknesses": "- The \"method\" section could be more organized, creating subsections could help. Moreover, the “zero-shot kernel estimation mechanism” and “auto-scale denoise guidance strategy”, should be explained in more detail, as these are the main contributions this paper makes. \n- The authors should justify using \"CSI\" (Critical Success Index) scores as the only evaluation metric. Adding another metric such as “Recall” (True positive rate) can complement the current evaluation and make it more comprehensive.", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)