Windset paper

.gitattributes

@@ -57,4 +57,3 @@ aviation_turbulence/training_data_high_fl.nc filter=lfs diff=lfs merge=lfs -text
 aviation_turbulence/training_data_low_fl.nc filter=lfs diff=lfs merge=lfs -text
 aviation_turbulence/training_data_med_fl.nc filter=lfs diff=lfs merge=lfs -text
 long_term_precipitation_forecast/training_data/patmosx/patmosx_19900101_00_00.nc filter=lfs diff=lfs merge=lfs -text
-*.grib2 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -2,127 +2,146 @@
 license: mit
 ---
 
+# Dataset Card for WINDSET
 
-# Dataset Card for WxC-Bench
-
-**WxC-Bench** primary goal is to provide a standardized benchmark for evaluating the performance of AI models in Atmospheric and Earth Sciences across various tasks.
+WINDSET is the Weather Insights and Novel Data for Systematic Evaluation and Testing dataset.
+WINDSET's goal is to provide a simple standard for evaluating the performance of Atmospheric and Earth Science AI over a range of tasks.
 
 ## Dataset Details
-
-WxC-Bench contains datasets for six key tasks:
-1. **Nonlocal Parameterization of Gravity Wave Momentum Flux**
-2. **Prediction of Aviation Turbulence**
-3. **Identifying Weather Analogs**
-4. **Generation of Natural Language Weather Forecasts**
-5. **Long-Term Precipitation Forecasting**
-6. **Hurricane Track and Intensity Prediction**
+WINDSET contains data for 6 tasks:
+- Nonlocal paramterization of gravity wave momentum flux
+- Prediction of aviation turbulence
+- Identifying weather analogs
+- Generating natural language forecasts
+- Long-term precipitation forecasting
+- Hurricane track and intensity prediction
 
 ### Dataset Description
 
-#### 1. Nonlocal Parameterization of Gravity Wave Momentum Flux
-The input variables consist of three dynamic atmospheric variables (zonal and meridional winds and potential temperature), concatenated along the vertical dimension. The output variables are the zonal and meridional components of vertical momentum flux due to gravity waves.
+<!-- Provide a longer summary of what this dataset is. -->
 
-- **Curated by:** [Aman Gupta](https://www.github.com/amangupta2)
-<!-- - **License:** MIT License -->
 
-#### 2. Generation of Natural Language Weather Forecasts
-The dataset includes the HRRR re-analysis data paired with NOAA Storm Prediction Center daily reports for January 2017. This task aims to generate human-readable weather forecasts.
 
-- **Curated by:** [NASA IMPACT](https://www.github.com/nasa-impact)
-<!-- - **License:** MIT License -->
+- **Curated by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** MIT License
 
-#### 3. Long-Term Precipitation Forecasting
-This dataset contains daily global rainfall accumulation records and corresponding satellite observations. The goal is to predict rainfall up to 28 days in advance.
+### Dataset Sources [optional]
 
-- **Curated by:** [Simon Pfreundschuh](https://www.github.com/simonpf) (Colorado State University)
+<!-- Provide the basic links for the dataset. -->
 
-#### 4. Aviation Turbulence Prediction
-Aimed at detecting turbulence conditions that impact aviation safety.
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-- **Curated by:** [NASA IMPACT](https://www.github.com/nasa-impact)
-<!-- - **License:** MIT License -->
+## Uses
 
-#### 5. Hurricane Track and Intensity Prediction
-Provides HURDAT2 data for predicting hurricane paths and intensity changes.
+<!-- Address questions around how the dataset is intended to be used. -->
 
-- **Curated by:** [NASA IMPACT](https://www.github.com/nasa-impact)
-<!-- - **License:** MIT License -->
+### Direct Use
 
-#### 6. Weather Analog Search
-Data to identify analog weather patterns for improved forecasting.
+<!-- This section describes suitable use cases for the dataset. -->
 
-- **Curated by:** [NASA IMPACT](https://www.github.com/nasa-impact)
-<!-- - **License:** MIT License -->
+[More Information Needed]
 
-### Dataset Sources
+### Out-of-Scope Use
 
-#### Nonlocal Parameterization of Gravity Wave Momentum Flux
-Developed using ERA5 reanalysis data (top 15 pressure levels above 1 hPa are excluded). Inputs were coarsely grained from winds and temperatures on a 0.3° grid.
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
 
-#### Long-Term Precipitation Forecasting
-Precipitation data sources include the PERSIANN CDR dataset (until June 2020) and IMERG final daily product. Satellite observations are sourced from PATMOS-x, GridSat-B1, and SSMI(S) brightness temperatures CDRs, with baseline forecasts from ECMWF and the UK Met Office S2S database.
+[More Information Needed]
 
 ## Dataset Structure
 
-WxC-Bench datasets are organized by task directories:
-| WxC-Bench           |
-|---------------------|
-| aviation_turbulence |
-| nonlocal_parameterization |
-| weather_analogs |
-| hurricane |
-| weather_forecast_discussion |
-| long_term_precipitation_forecast |
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
 
-Each directory contains datasets specific to the respective downstream tasks.
+[More Information Needed]
 
 ## Dataset Creation
 
 ### Curation Rationale
-The WxC-Bench dataset aims to create a unified standard for assessing AI models applied to complex meteorological and atmospheric science tasks.
+
+<!-- Motivation for the creation of this dataset. -->
+
+[More Information Needed]
 
 ### Source Data
 
-The datasets were created using multiple authoritative data sources, such as ERA5 reanalysis data, NOAA Storm Prediction Center reports, PERSIANN CDR, and IMERG products. Data processing involved spatial and temporal alignment, quality control, and variable normalization.
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+#### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+[More Information Needed]
+
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+[More Information Needed]
+
+### Annotations [optional]
+
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+
+#### Annotation process
+
+<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+[More Information Needed]
+
+#### Personal and Sensitive Information
 
-## Citation
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-```
-@misc{shinde2024wxcbenchnoveldatasetweather,
-      title={WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks}, 
-      author={Rajat Shinde and Christopher E. Phillips and Kumar Ankur and Aman Gupta and Simon Pfreundschuh and Sujit Roy and Sheyenne Kirkland and Vishal Gaur and Amy Lin and Aditi Sheshadri and Udaysankar Nair and Manil Maskey and Rahul Ramachandran},
-      year={2024},
-      eprint={2412.02780},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2412.02780}, 
-}
-```
-
-## Dataset Card Authors
-
-- Rajat Shinde
-- Christopher E. Phillips
-- Sujit Roy
-- Ankur Kumar
-- Aman Gupta
-- Simon Pfreundschuh
-- Sheyenne Kirkland
-- Vishal Gaur
-- Amy Lin
-- Aditi Sheshadri
-- Manil Maskey
-- Rahul Ramachandran
+[More Information Needed]
 
-## Dataset Card Contact
+**APA:**
 
-For each task, please contact:
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Dataset Card Authors [optional]
+
+[More Information Needed]
+
+## Dataset Card Contact
 
-- **Nonlocal Parameterization of Gravity Wave Momentum Flux:** [Aman Gupta](https://www.github.com/amangupta2)
-- **Aviation Turbulence Prediction:** [Christopher E. Phillips](https://www.github.com/sodoesaburningbus)
-- **Identifying Weather Analogs:** Christopher E. Phillips, Rajat Shinde
-- **Natural Language Weather Forecasts:** [Rajat Shinde](https://www.github.com/omshinde), Sujit Roy
-- **Long-Term Precipitation Forecasting:** [Simon Pfreundschuh](https://www.github.com/simonpf)
-- **Hurricane Track and Intensity Prediction:** [Ankur Kumar](https://www.github.com/ankurk017)
+[More Information Needed]

Weather_Analogs/README.md

@@ -1,36 +0,0 @@
-# Dataset Card for Weather Analog Search
-
-## Dataset Description
-This dataset contains processed MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) weather data focused on Western Europe. It includes two key variables:
-- Sea Level Pressure (SLP)
-- 2-meter Temperature (T2M)
-
-The data covers a geographic region bounded by:
-- Longitude: -15° to 0° 
-- Latitude: 42° to 58°
-
-## Time Coverage
-- Start Date: January 1, 2019
-- End Date: December 31, 2021
-- Temporal Resolution: Daily
-
-## Data Format
-- NetCDF4 files
-- Each file contains a single day of data
-- File naming convention: MERRA2_SLP_T2M_YYYYMMDD.nc
-
-## Variables
-- SLP: Sea Level Pressure
-- T2M: 2-meter Temperature
-
-## Geographic Coverage
-The dataset covers Western Europe with:
-- 25 longitude points (-15° to 0°)
-- 33 latitude points (42° to 58°)
-- Spatial resolution: ~0.625° x 0.5°
-
-## Data Source
-The data is derived from NASA's MERRA2 reanalysis dataset, processed to extract specific variables and geographic region of interest.
-
-## Intended Use
-This dataset is designed for weather analog search applications, allowing users to find historical weather patterns similar to current conditions in Western Europe.

Weather_Analogs/weather_analog.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fad10c9ace377de7817ea1f61618f3632a375181cf8be342dcae75db702f7cc4
-size 42235592

aviation_turbulence/dataset.py

@@ -1,63 +0,0 @@
-import os
-import datasets
-
-class AviationTurbulence(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        """
-        Defines the dataset metadata and feature structure.
-        """
-        return datasets.DatasetInfo(
-            description="Dataset containing .nc files for training.",
-            features=datasets.Features({
-                "file_path": datasets.Value("string"),  # Store file paths
-            }),
-            supervised_keys=None,  # Update if supervised task is defined
-            homepage="https://huggingface.co/datasets/nasa-impact/WINDSET/tree/main/aviation_turbulence",
-            license="MIT",
-        )
-
-    def _split_generators(self, dl_manager):
-        """
-        Define the dataset splits for train.
-        """
-        # Define the directory containing the dataset
-        data_dir = os.path.join(os.getcwd(), "aviation_turbulence")  # Update with the actual directory
-
-        # Get the directory for the train split (no validation or test splits)
-        train_dir = os.path.join(data_dir)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"split_dir": train_dir},
-            ),
-        ]
-
-    def _generate_data_from_files(self, data_dir):
-        """
-        Generate file paths for each .nc file in the directory.
-        """
-        example_id = 0
-
-        # Loop through the files in the directory
-        for nc_file in os.listdir(data_dir):
-
-            if nc_file.endswith(".nc"):  
-                nc_file_path = os.path.join(data_dir, nc_file)
-
-                yield example_id, {
-                    "file_path": nc_file_path,
-                }
-                example_id += 1
-            else:
-                pass
-
-    def _generate_examples(self, split_dir):
-        """
-        Generates examples for the dataset from the split directory.
-        """
-        # Call the data generator to get the file paths
-        for example_id, example in self._generate_data_from_files(split_dir):
-            yield example_id, example

hurricane/2021.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9597c2335ac6b317056d5944142b72872d623cc8bb4aea326962d4a5676756f2
-size 46017333248

hurricane/2022.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4748f596595d38587b502fa3c2d2ee1f87f3bfeda585a0f34a8f738f17dbafaa
-size 46017333248

hurricane/dataset.py

@@ -1,63 +0,0 @@
-import os
-import datasets
-
-class HurricaneDetection(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        """
-        Defines the dataset metadata and feature structure.
-        """
-        return datasets.DatasetInfo(
-            description="Dataset containing .nc files for training.",
-            features=datasets.Features({
-                "file_path": datasets.Value("string"),  # Store file paths
-            }),
-            supervised_keys=None,  # Update if supervised task is defined
-            homepage="https://huggingface.co/datasets/nasa-impact/WINDSET/tree/main/hurricane",
-            license="MIT",
-        )
-
-    def _split_generators(self, dl_manager):
-        """
-        Define the dataset splits for train.
-        """
-        # Define the directory containing the dataset
-        data_dir = os.path.join(os.getcwd(), "hurricane")  # Update with the actual directory
-
-        # Get the directory for the train split (no validation or test splits)
-        train_dir = os.path.join(data_dir)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"split_dir": train_dir},
-            ),
-        ]
-
-    def _generate_data_from_files(self, data_dir):
-        """
-        Generate file paths for each .h5 file in the directory.
-        """
-        example_id = 0
-
-        # Loop through the files in the directory
-        for h5_file in os.listdir(data_dir):
-
-            if h5_file.endswith(".h5"):  
-                h5_file_path = os.path.join(data_dir, h5_file)
-
-                yield example_id, {
-                    "file_path": h5_file_path,
-                }
-                example_id += 1
-            else:
-                pass
-
-    def _generate_examples(self, split_dir):
-        """
-        Generates examples for the dataset from the split directory.
-        """
-        # Call the data generator to get the file paths
-        for example_id, example in self._generate_data_from_files(split_dir):
-            yield example_id, example

hurricane/variable_list.csv

@@ -1,39 +0,0 @@
-,parameter,full_name,level,units
-0,U10m,Zonal Wind at 10m,Surface,m/s
-1,V10m,Meridional Wind at 10m,Surface,m/s
-2,T2m,Temperature at 2m,2 meters,K
-3,SLP,Sea Level Pressure,Sea Level,hPa
-4,QV2m,Specific Humidity at 2m,2 meters,kg/kg
-5,TQI,Total Precipitable Ice Water,Integrated,kg/m2
-6,TQL,Total Precipitable Liquid Water,Integrated,kg/m2
-7,TQV,Total Precipitable Water Vapor,Integrated,kg/m2
-8,U63,Zonal Wind at eta level 63,850 hPa,m/s
-9,U56,Zonal Wind at eta level 56,700 hPa,m/s
-10,U50,Zonal Wind at eta level 50,487 hPa,m/s
-11,U44,Zonal Wind at eta level 44,244 hPa,m/s
-12,U39,Zonal Wind at eta level 39,108 hPa,m/s
-13,V63,Meridional Wind at eta level 63,850 hPa,m/s
-14,V56,Meridional Wind at eta level 56,700 hPa,m/s
-15,V50,Meridional Wind at eta level 50,487 hPa,m/s
-16,V44,Meridional Wind at eta level 44,244 hPa,m/s
-17,V39,Meridional Wind at eta level 39,108 hPa,m/s
-18,T63,Temperature at eta level 63,850 hPa,K
-19,T56,Temperature at eta level 56,700 hPa,K
-20,T50,Temperature at eta level 50,487 hPa,K
-21,T44,Temperature at eta level 44,244 hPa,K
-22,T39,Temperature at eta level 39,108 hPa,K
-23,QV63,Specific Humidity at eta level 63,850 hPa,kg/kg
-24,QV56,Specific Humidity at eta level 56,700 hPa,kg/kg
-25,QV50,Specific Humidity at eta level 50,487 hPa,kg/kg
-26,QV44,Specific Humidity at eta level 44,244 hPa,kg/kg
-27,QV39,Specific Humidity at eta level 39,108 hPa,kg/kg
-28,OMEGA63,Vertical Velocity at eta level 63,850 hPa,Pa/s
-29,OMEGA56,Vertical Velocity at eta level 56,700 hPa,Pa/s
-30,OMEGA50,Vertical Velocity at eta level 50,487 hPa,Pa/s
-31,OMEGA44,Vertical Velocity at eta level 44,244 hPa,Pa/s
-32,OMEGA39,Vertical Velocity at eta level 39,108 hPa,Pa/s
-33,H63,Geopotential Height at eta level 63,850 hPa,m
-34,H56,Geopotential Height at eta level 56,700 hPa,m
-35,H50,Geopotential Height at eta level 50,487 hPa,m
-36,H44,Geopotential Height at eta level 44,244 hPa,m
-37,H39,Geopotential Height at eta level 39,108 hPa,m

long_term_precipitation_forecast/dataset.py

@@ -1,99 +0,0 @@
-import os
-import datasets
-
-class LongTermPrecipitationDataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        """
-        Defines the dataset metadata and feature structure.
-        """
-        return datasets.DatasetInfo(
-            description="Dataset containing .nc files per year for variables.",
-            features=datasets.Features({
-                "file_path": datasets.Value("string"),  # Store file paths
-                "year": datasets.Value("string"),       # Track year
-                "subfolder": datasets.Value("string")   # Track subfolder (sf1, sf2)
-            }),
-            supervised_keys=None,  # Update if supervised task is defined
-            homepage="https://huggingface.co/datasets/nasa-impact/WINDSET/tree/main/long_term_precipitation_forecast",
-            license="MIT",
-        )
-
-    def _split_generators(self, dl_manager):
-        """
-        Define the dataset splits for train, validation, and test.
-        """
-        # Define the directory containing the dataset
-        data_dir = os.path.join(os.getcwd(), "long_term_precipitation_forecast")
-
-        # Get the directories for each split
-        train_dir = os.path.join(data_dir, "training_data")
-        validation_dir = os.path.join(data_dir, "validation_data")
-        test_dir = os.path.join(data_dir, "test_data")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"split_dir": train_dir},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"split_dir": validation_dir},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"split_dir": test_dir},
-            ),
-        ]
-
-    def _get_subfolders(self, base_dir):
-        """
-        Get all subfolders from the base directory.
-        """
-        return [os.path.join(base_dir, subfolder) for subfolder in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, subfolder))]
-
-    def _get_year_folders(self, subfolder_dir):
-        """
-        Get all year folders inside a subfolder.
-        """
-        return [os.path.join(subfolder_dir, year_folder) for year_folder in os.listdir(subfolder_dir) if os.path.isdir(os.path.join(subfolder_dir, year_folder))]
-
-    def _generate_data_from_files(self, data_dir):
-        """
-        Generate file paths for each subfolder, year, and daily file.
-        """
-        example_id = 0
-
-        # Loop through subfolders
-        for subfolder in os.listdir(data_dir):
-            subfolder_path = os.path.join(data_dir, subfolder)
-
-            if os.path.isdir(subfolder_path):
-                # Loop through year folders inside the subfolder
-                for year_folder in os.listdir(subfolder_path):
-                    year_folder_path = os.path.join(subfolder_path, year_folder)
-
-                    if os.path.isdir(year_folder_path):
-                        # Loop through daily files inside the year folder
-                        for daily_file in os.listdir(year_folder_path):
-                            daily_file_path = os.path.join(year_folder_path, daily_file)
-
-                            if daily_file.endswith(".nc"):  # Only select NetCDF files
-                                # Yield file information for each data point
-                                yield example_id, {
-                                    "file_path": daily_file_path,
-                                    "year": year_folder,
-                                    "subfolder": subfolder,
-                                }
-                                example_id += 1
-                            else:
-                                raise FileNotFoundError(f"{daily_file_path} not found")
-
-    def _generate_examples(self, split_dir):
-        """
-        Generates examples for the dataset from the split directory.
-        """
-        # Call the data generator to get the file paths
-        for example_id, example in self._generate_data_from_files(split_dir):
-            yield example_id, example

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190101_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bc4af780640fdc4e129a8df246a89f2515c2b59d0c9e69782e09bd4a681c28d1
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190102_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a3ae15cc38a5d2e0d8ba58aa1dc658599dc6668513ba96283d494f6524858aee
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190103_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8e0fd0a0a56adbea95efdfe609f9ac08280fd0e4124ded19b3a347718d6e1eba
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190104_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3df2e0669d6c54ef4db2a5e89bf6c527bcca312081d8baa04960ca73e5eb8ae9
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190105_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9e47d6b304112845ae596cd5041f496548808100fd46f400ce46e14713fd13de
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190106_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5c71e2a3430da20162ac3f78fa1739160b11b0338150ddba6630b9e0b961c9f5
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190107_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6fc71735f1f95e4b97e8fb53c60390b23b35c714a9fa490716c97b039d0e0fec
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190108_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e68e6d86d23488a0a4d1087117407fce1d9272eec1b160a39a46b30e904d11eb
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190109_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b5d28117d865c6884fcb16bd2bb2f83ff7406b3c4da311a741a1c0c9b9351f68
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190110_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bafdc5ce330ceb0d911b32db971a0f7a650cc3bafb501ba6ae02966cb9ceb030
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190111_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4567f5551d7af38642d95d353cd041a12b20bd9d56e0c1d3de718a3b2a932b81
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190112_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d6d197396c54ffc0fe84bf9706094ce09bcb628a5dc9df500588462febc72afe
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190113_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:15bb0d3088fdbf99071453a10ca0dff66f4cfcb4f00a1a4f49f08d219f959959
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190114_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a4d95770dbbd1f3cca38a91d119d486a6f5a25b1b7386badfbb608d35057f2d4
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190115_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b18558a36a4fbda4359a9c91a3553c5971140831bb9b6508c46342441e634cdf
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190116_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8add46a97d7bc3a85988112123ce6a285afb2c976be67d9a623a9869e4a9aead
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190117_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fc71070214e6ebb7e093c4778e4fd8f336e0095d74959d816df347b88353bd21
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190118_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a1e870fb63fcb9dbb3519bd379f1e3cb5533d3d8f785896abc574b5e4c9c99a9
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190119_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:75418179bcdd1f1c9f96faa95fa5cf53378de80f0fa967c218ea3cc5ad4b1853
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190120_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:522e5922434eef128b0184bfebefdc8145d4c40606176e690f6868ddbdc31e0a
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190121_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5fff0795ca8cb6f4f404d42a196d403920e472316fc50b729c6004347242c263
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190122_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:88da0d37a3307713469f2b019704fdc4db80add6e5d39fc61e0625a500d0d239
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190123_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:817878342ec6c941356e7880ccf6303bd81d001555a46a0a80b463f2a80c2f70
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190124_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6a77eacbd9d9ca012e94c6cebf7c20ed49b37957755dd16c723e6d65c288e2be
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190125_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7d6c72c75d6f95a0a99ff252ebf8c35f2f27ffcb3301fd2e9c05186222535de4
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190126_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e75a099754e25d92f811f1c1e39efb2fefc2106388ed4447d67fcba20dbc2131
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190127_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:75f1efa96cff33cd2c42f32a67d064d1c6957def593992ae6297e1880c1db11e
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190128_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ad0d189f1c0de348d50c869c2b0f5d2f91addc546c27eb8ad8504a2d7c4340e2
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190129_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b0ad5754a6c8e834e396090393ef7c53c7508fcd2e90164b8faebcd28a0568ff
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190130_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:04d50f2fe511d2f3ed2a8ece584c14c8ad98c284f79af4be318043123e0926e2
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190131_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6535553c3342d7399b3bdc1f7fcd6bb4b860518433f162cde45c2ba0d5c88ac4
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190201_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b0c4243ac4f930aac9c4ccdc4dda9c3ae82ae6c82501372cb85ce2aa5cd0d0d3
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190202_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ee23a1079a38ccae3675b1efb651a5ca3f14787e065ecb283f7c739f6962ea68
-size 841384

long_term_precipitation_forecast/test_data/daily_precip/2019/precip_20190203_00_00.nc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d15d669c4040942ea63b2921aaa5aef6ec403a9c681469aaac099618513e3058
-size 841384