Merge branch 'develop' into black-belt

.env

@@ -8,10 +8,10 @@ APP_DESCRIPTION=Visualization, Exploration, and Data Analysis (VEDA)
 APP_CONTACT_EMAIL=email@example.org
 
 # Endpoint for the Tiler server. No trailing slash.
-API_RASTER_ENDPOINT='https://staging.openveda.cloud/api/raster'
+API_RASTER_ENDPOINT='https://openveda.cloud/api/raster'
 
 # Endpoint for the STAC server. No trailing slash.
-API_STAC_ENDPOINT='https://staging.openveda.cloud/api/stac'
+API_STAC_ENDPOINT='https://openveda.cloud/api/stac'
 API_XARRAY_ENDPOINT='https://prod-titiler-xarray.delta-backend.com/tilejson.json'
 
 MAPBOX_STYLE_URL='mapbox://styles/covid-nasa/ckb01h6f10bn81iqg98ne0i2y'
@@ -27,3 +27,8 @@ GOOGLE_FORM = 'https://docs.google.com/forms/d/e/1FAIpQLSfGcd3FDsM3kQIOVKjzdPn4f
 GOOGLE_ANALYTICS_ID='G-CQ3WLED121'
 
 FEATURE_NEW_EXPLORATION = 'TRUE'
+
+SHOW_CONFIGURABLE_COLOR_MAP = 'TRUE'
+
+CUSTOM_SCRIPT_SRC='https://dap.digitalgov.gov/Universal-Federated-Analytics-Min.js?agency=NASA&subagency=HQ'
+CUSTOM_SCRIPT_ID='_fed_an_ua_tag'

.github/CODEOWNERS

@@ -0,0 +1,5 @@
+# These owners will be the default owners for everything in
+# the repo. Unless a later match takes precedence,
+# these will be requested for
+# review when someone opens a pull request.
+* @hanbyul-here @sandrahoang686 @dzole0311 @aboydnw @AliceR @snmln

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,7 +1,22 @@
-<!-- -----------^ Click "Preview" for a functional view! -->
+## PR Checklists
+(Select the checklist that applies to your PR)
 
-## Why are you creating this Pull Request?
+### Adding a Dataset Overview:
+- [ ] Dataset is available in the production VEDA STAC
+- [ ] Production STAC url is referenced in the dataset overview
+- [ ] All metadata, including those for the E&A page, is completed in the mdx file
+- [ ] Any images are compressed, ideally <500 KB
+- [ ] At least one code owner has reviewed the PR
 
-- [Adding Datasets or Stories](?title=Content%3A%20%3Cname%3E&expand=1&template=content.md)
-- [Version Release](?title=Deploy%20vX.X.X&expand=1&template=version_release.md)
-- [Other](?expand=1&template=default.md)
+### Adding a Data Story:
+- [ ] Any data referenced in the story is already published to VEDA STAC and referenced in a separate dataset overview page (prior to merging or as part of the same PR)
+- [ ] All metadata is completed in the mdx file
+- [ ] Any images are compressed, ideally <500 KB
+- [ ] Published Date is as close to the actual release date as possible (work with veda-ui team to determine potential release date when story is close to ready)
+- [ ] At least one code owner has reviewed the PR
+
+### Updating VEDA-UI
+- [ ] Confirmed that [updating the `veda-ui` submodule](https://github.com/NASA-IMPACT/veda-config/blob/main/docs/DEVELOPMENT.md#development) is needed.
+- [ ] Ensured that the correct version of veda-ui is being used
+- [ ] Reviewed the veda-ui changelogs for relevant updates
+- [ ] Tested that the changes work as expected with the current instance configuration

.github/workflows/deploy-prod.yml

@@ -13,7 +13,7 @@ on:
 
 env:
   NODE: 20
-  DOMAIN_PROD: https://www.earthdata.nasa.gov/dashboard
+  DOMAIN_PROD: /dashboard
   DEPLOY_BUCKET_PROD: climatedashboard
   DEPLOY_BUCKET_PROD_REGION: us-east-1
 

datasets/CMIP-winter-median-pr.data.mdx

@@ -11,7 +11,14 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Precipitation
+      - Snow
+      - Climate
+      - Climate Model
+  - name: Source
+    values:
+      - NASA EIS
+      - CMIP6
 infoDescription: |
   ::markdown
     Future changes to precipitation are expected to alter the volume and timing of snow water resources. Here, we present the projected percent-change to Western US cumulative winter precipitation at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the CMIP6 NASA Earth Exchange Global Daily Downscaled Projections.
@@ -34,7 +41,7 @@ layers:
       layerId: CMIP245-winter-median-pr
       mapLabel: |
         ::js ({ dateFns, datetime, compareDatetime }) => {
-          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'DD LLL yyyy')}`;        
+          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;        
         }
     legend:
       type: gradient
@@ -74,7 +81,7 @@ layers:
       layerId: CMIP585-winter-median-pr
       mapLabel: |
         ::js ({ dateFns, datetime, compareDatetime }) => {
-          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
+          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
         }
     legend:
       type: gradient

datasets/CMIP-winter-median-ta.data.mdx

@@ -11,7 +11,15 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Temperature
+      - Precipitation
+      - Snow
+      - Climate
+      - Climate Model
+  - name: Source
+    values:
+      - NASA EIS
+      - CMIP6
 infoDescription: |
   ::markdown
     Future changes to air temperature are expected to influence the phase of winter precipitation (snowfall or rainfall) and the timing and amount of snowmelt and streamflow. Here, we present the projected percent-change to Western US average winter temperature at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the [CMIP6 NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).
@@ -33,7 +41,7 @@ layers:
       layerId: CMIP245-winter-median-ta
       mapLabel: |
         ::js ({ dateFns, datetime, compareDatetime }) => {
-          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'DD LLL yyyy')}`;        
+          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;        
         }
     legend:
       unit:
@@ -74,7 +82,7 @@ layers:
       layerId: CMIP585-winter-median-ta
       mapLabel: |
         ::js ({ dateFns, datetime, compareDatetime }) => {
-          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'DD LLL yyyy')}`;
+          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
         }
     legend:
       unit:

datasets/FLDAS-soilmoisture-anomalies.data.mdx

@@ -43,7 +43,7 @@ layers:
       layerId: SoilMoi00_10cm_tavg
       mapLabel: |
         ::js ({ dateFns, datetime, compareDatetime }) => {
-          return `${dateFns.format(datetime, 'DD LLL yyyy')}`;        
+          return `${dateFns.format(datetime, 'dd LLL yyyy')}`;        
         }
     legend:
       unit:

datasets/GPM_3IMERGDF.data.mdx

@@ -0,0 +1,75 @@
+---
+id: GPM_3IMERGDF.v07
+name: "GPM IMERG Daily Precipitation"
+description: "GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree"
+media:
+  src: ::file ./gpmimergdaily.png
+  alt: CMIP6 Near-Surface Air Temperature Screenshot
+  author:
+    name: NASA
+    url:
+taxonomy:
+  - name: Topics
+    values:
+      - Climate
+layers:
+  - id: GPM_3IMERGDF.v07
+    type: cmr
+    stacCol: GPM_3IMERGDF
+    tileApiEndpoint: "https://dev-titiler-cmr.delta-backend.com/WebMercatorQuad/tilejson.json"
+    name: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree
+    description: "GPM Level 3 IMERG Final Daily 10 x 10 km (GPM_3IMERGDF) accumulated precipitation"
+    time_density: day
+    zoomExtent:
+      - 0
+      - 20
+    sourceParams:
+      tile_scale: 3
+      resampling: bilinear
+      variable: precipitation
+      colormap_name: gnbu
+      rescale:
+        - 0
+        - 46
+      maxzoom: 12
+      concept_id: C2723754864-GES_DISC
+      backend: xarray
+    legend:
+      unit:
+        label:
+      type: gradient
+      min: "0 mm/hr"
+      max: "46 mm/hr"
+      stops:
+        [
+          "#f7fcf0",
+          "#e6f5e1",
+          "#d7efd1",
+          "#c5e8c2",
+          "#abdeb6",
+          "#8bd2bf",
+          "#6bc3c9",
+          "#4bafd1",
+          "#3193c2",
+          "#1878b4",
+          "#085da0",
+          "#084081",
+        ]
+---
+
+<Block>
+<Prose>
+# GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06 (GPM_3IMERGDF)
+
+## Dataset Description
+
+This dataset is the GPM Level 3 IMERG _Final_ Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24.
+
+Source: [https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary)
+
+## Data Source
+
+The files represented are NetCDF files in GES DISC's Earthdata Cloud bucket and discovered and tiled via a dynamic tiling service using CMR.
+
+</Prose>
+</Block>

datasets/aerosol-difference.data.mdx

@@ -12,6 +12,9 @@ taxonomy:
   - name: Topics
     values:
       - Air Quality
+  - name: Source
+    values:
+      - MODIS
 infoDescription: |
   ::markdown
     This dataset comes from the two decadal COGs that displayed mean Aerosol Optical Depth for 2000-2009 and for 2010-2019. Those tiffs were subtracted to display the differences between the two decades.

datasets/bangladesh-landcover-2001-2020.data.mdx

@@ -11,10 +11,13 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Land Cover
+  - name: Source
+    values:
+      - MODIS
 infoDescription: |
   ::markdown
-     The annual land cover maps of 2001 and 2021 were captured using combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset (MCD12Q1 V6, dataset link: [https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)).  The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country.
+     The annual land use - land cover maps for 2001 and 2021 were captured using the combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset ([MCD12Q1 V6](https://lpdaac.usgs.gov/products/mcd12q1v006/)).  The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country.
 layers:
   - id: bangladesh-landcover-2001-2020
     stacCol: bangladesh-landcover-2001-2020

datasets/barc-thomasfire.data.mdx

@@ -11,7 +11,10 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+  - name: Source
+    values:
+      - NASA EIS
 infoDescription: |
   ::markdown
     Burn Area Reflectance Classification (BARC) from the Burned Area Emergency Response (BAER) program for the Thomas Fire of 2017.

datasets/caldor-fire-characteristics-burn-severity.data.mdx

@@ -11,7 +11,10 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+  - name: Source
+    values:
+      - NASA EIS
 infoDescription: |
   ::markdown
     This dataset describes the progression and active fire behavior of the 2021 Caldor Fire in California, as recorded by the algorithm detailed in https://www.nature.com/articles/s41597-022-01343-0. It includes an extra layer detailing the soil burn severity (SBS) conditions provided by the [Burned Area Emergency Response](https://burnseverity.cr.usgs.gov/baer/) team.

datasets/camp-fire-albedo-wsa-diff.data.mdx

@@ -11,7 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+      - Disasters
+      - Land Cover
+  - name: Source
+    values:
+      - MODIS
 infoDescription: |
   ::markdown
     In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the Albedo WSA difference portion of that analysis.

datasets/camp-fire-lst-day-diff.data.mdx

@@ -11,7 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+      - Disasters
+      - Temperature
+  - name: Source
+    values:
+      - MODIS
 infoDescription: |
   ::markdown
      In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Day difference portion of that analysis.

datasets/camp-fire-lst-night-diff.data.mdx

@@ -11,7 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+      - Disasters
+      - Temperature
+  - name: Source
+    values:
+      - MODIS
 infoDescription: |
   ::markdown
     In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Night difference portion of that analysis.

datasets/camp-fire-ndvi-diff.data.mdx

@@ -11,7 +11,12 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Wildfire
+      - Disasters
+      - Land Cover
+  - name: Source
+    values:
+      - MODIS
 infoDescription: |
   ::markdown
     In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the NDVI difference portion of that analysis.    

datasets/camp-fire-nlcd.data.mdx

@@ -11,7 +11,10 @@ media:
 taxonomy:
   - name: Topics
     values:
-      - EIS
+      - Land Cover
+  - name: Source
+    values:
+      - Landsat
 infoDescription: |
   ::markdown
     We utilized the National Land Cover Database (NLCD), which provides a classification of land cover categories at 30m spatial resolution over geographical locations within the Continental United States (CONUS). The NLCD is derived from Landsat satellite sensors data and is available at approximately three-year time intervals. We used the NLCD maps for the years 2016 and 2019 to examine changes in land cover type resulting from the Camp Fire event, to examine LULC before and after the Camp Fire. This analysis shows that the dominant vegetation cover type that was present within the region per-wildfire are evergreen forest and shrub/scrub cover, while post-wildfire are grasslands and herbaceous vegetation.

datasets/cmip6-tas.data.mdx

@@ -1,6 +1,6 @@
 ---
 id: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO
-name: 'CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (demo subset)'
+name: 'Historic CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (1950-2014)'
 featured: false
 description: "Daily near-surface air temperature from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) Project."
 media:
@@ -13,11 +13,16 @@ taxonomy:
   - name: Topics
     values:
       - Climate
+      - Temperature
+  - name: Source
+    values:
+      - CMIP6
+
 infoDescription: |
   ::markdown
     * Format: [kerchunk (metadata)](https://fsspec.github.io/kerchunk/) for netCDF4
     * Spatial Coverage: 180° W to 180° E, 60° S to 90° N
-    * Temporal: 1950-01-01 to 1951-12-31
+    * Temporal: 1950-01-01 to 2014-12-31
       * _As noted below, this dataset is a subset all available data. The full dataset includes data from 1950 to 2100._
     * Data Resolution:
       * Latitude Resolution: 0.25 degrees (25 km)
@@ -26,10 +31,10 @@ infoDescription: |
 layers:
   - id: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO
     stacCol: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO
-    name: CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (demo subset)
+    name: Historic CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (1950-2014)
     type: zarr
     tileApiEndpoint: 'https://prod-titiler-xarray.delta-backend.com/tilejson.json'
-    description: "Historical (1950-2014) daily-mean near-surface (usually, 2 meter) air temperature in Kelvin."
+    description: "Historical CMIP6 (1950-2014) daily-mean near-surface (2 meter) air temperature in Kelvin."
     zoomExtent:
       - 0
       - 20
@@ -72,7 +77,7 @@ NEX-GDDP-CMIP6 is comprised of global downscaled climate scenarios derived from
 
 * Format: [kerchunk (metadata)](https://fsspec.github.io/kerchunk/) for netCDF4
 * Spatial Coverage: 180° W to 180° E, 60° S to 90° N
-* Temporal: 1950-01-01 to 1951-12-31
+* Temporal: 1950-01-01 to 2014-12-31
   * _As noted below, this dataset is a subset all available data. The full dataset includes data from 1950 to 2100._
 * Data Resolution:
   * Latitude Resolution: 0.25 degrees (25 km)