Weird Weather In Washington State

Authors

Masha Vernik

Camilla Green

Juan Martinez

NLU Students

Weird Weather: Precipitation and Temperature Variability for Assessing Agricultural Vulnerability to Climate Change

Overview

Objectives

Introduction

Getting Started with Google Earth Engine (GEE)

Google Earth Engine (GEE) requires registration. Since this is for educational purposes, a pricing plan is not required for noncommercial registration. For more information accessing GEE, please visit Earth Engine Access.

Once you have registered and enabled the GEE API, we can now work with GEE in Python!

For more information, please see An Intro to the Earth Engine Python API

First of all, run the following cell to initialize the API. The output will contain instructions on how to grant this notebook access to Earth Engine using your account. To find out the project ID, Go to https://console.cloud.google.com/; In the top bar, click the dropdown next to the current project name. You’ll see a list of projects. The Project ID is in the second column.

import ee

# Trigger the authentication flow.
ee.Authenticate()

# Initialize the library.
ee.Initialize(project='PROJECT_ID') #replpace with your project ID

Hello from the Earth Engine servers!

The code above should generate a GEE token, which this application will request for you to paste.

Working with U.S. Census TIGER data

Python packages

This lesson uses the following Python packages to work with Google Earth Engine: ee, earthengine-api, pandas, numpy

import folium
import matplotlib.pyplot as plt
import pandas as pd
import datetime
import branca.colormap as cm

# U.S. Census counties (2020)
counties = ee.FeatureCollection("TIGER/2018/Counties")

# Filter to Washington State (STATEFP = '53')
wa_counties = counties.filter(ee.Filter.eq("STATEFP", "53"))

# # Create the map
# Map1 = geemap.Map(center=[47.5, -120], zoom=6)

# # Add Washington counties layer
# Map1.addLayer(wa_counties, {}, "Washington Counties")

# # Display the map
# Map1


# Convert the Earth Engine FeatureCollection to GeoJSON
wa_counties_geojson = ee.FeatureCollection(wa_counties).getInfo()

# Create a folium map
Map1 = folium.Map(location=[47.5, -120], zoom_start=6)

# Add the counties as a GeoJSON overlay
folium.GeoJson(
    wa_counties_geojson,
    name="Washington Counties"
).add_to(Map1)

# Add layer control
folium.LayerControl().add_to(Map1)

# Display the map
Map1

Make this Notebook Trusted to load map: File -> Trust Notebook

Importing ERA5 Land Daily Aggregated image collection

The GEE Data Catalog is a great source for planetary-scale Earth science data.

From this catalog, we will be working with the ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis.

#Import data
era5 = ee.ImageCollection("ECMWF/ERA5_LAND/DAILY_AGGR")

# Filter to years and selected bands
era5 = era5.filterDate('2009-01-01', '2024-12-31').select('temperature_2m', 'total_precipitation_sum')

# Clip each image to Washington state (functionally map over the collection)
era5 = era5.map(lambda img: img.clip(wa_counties))

Filter Data from ERA5

# Filter to list of years 

# Creating list of normals years for ERA 5 land
era5_hist = era5.filterDate('2009-01-01', '2023-12-31')


# Creating list of days for the year
era5_2024 = era5.filterDate('2024-01-01', '2024-12-31')

Calculate Normals from 2009 - 2023

Loop through each of the 10 years: 2009-2013 - Select only relevant years for that iteration of the loop - Create average for each day for each year - create image collection that contains an image with averages for each day for the selected years - for each image in image collection, reduce to zip code average - return list of averages for each zip for each day in those selected years

# Function to get average values for each county in a FeatureCollection
# Safer Avg function with fallback scale
# Safe average function with fixed scale and GEOID to county_id
def Avg(image, counties, fallback_scale=10000):
    # Safely get scale (if projection is valid)
    scale = ee.Number(fallback_scale)
    try:
        proj_scale = image.projection().nominalScale()
        scale = ee.Algorithms.If(proj_scale, proj_scale, scale)
    except:
        pass  # fallback already defined

    # Reduce image over counties
    stats = image.reduceRegions(
        collection=counties,
        reducer=ee.Reducer.mean(),
        scale=scale
    )

    # Ensure 'county_id' property is present (based on GEOID)
    def add_ids(f):
        return f.set('county_id', f.get('GEOID'))

    return stats.map(add_ids)

# Get both county GEOIDs and names
county_ids = wa_counties.aggregate_array('GEOID').getInfo()
county_names = wa_counties.aggregate_array('NAME').getInfo()

# Initialize DataFrames
temps_1 = pd.DataFrame({'county_id': county_ids, 'county_name': county_names})
precips_1 = pd.DataFrame({'county_id': county_ids, 'county_name': county_names})

# Loop through days of year (DOY 121–123, i.e., May 1–3 for testing)
for day in range(121, 124): #182
    day_str = str(day)

    # 1. Get composite average for this DOY across all years
    day_data = era5_hist.filter(ee.Filter.calendarRange(day, day, 'day_of_year'))
    composite = day_data.mean()

    # 2. Reduce over counties
    reduced = composite.reduceRegions(
        collection=wa_counties,
        reducer=ee.Reducer.mean(),
        scale=10000  # ERA5 resolution
    )

    # Add county_id and county_name to each feature
    def add_county_info(f):
        return f.set({
            'county_id': f.get('GEOID'),
            'county_name': f.get('NAME')
        })

    reduced = reduced.map(add_county_info)

    # 3. TEMPERATURE EXTRACTION
    temp_data = reduced.map(lambda f: ee.Feature(None, {
        'county_id': f.get('county_id'),
        'county_name': f.get('county_name'),
        day_str: f.get('temperature_2m')
    })).reduceColumns(
        reducer=ee.Reducer.toList(3),
        selectors=['county_id', 'county_name', day_str]
    ).getInfo()['list']

    temp_df = pd.DataFrame(temp_data, columns=['county_id', 'county_name', day_str])
    temps_1 = pd.merge(temps_1, temp_df, on=['county_id', 'county_name'], how='left')

    # 4. PRECIPITATION EXTRACTION
    precip_data = reduced.map(lambda f: ee.Feature(None, {
        'county_id': f.get('county_id'),
        'county_name': f.get('county_name'),
        day_str: f.get('total_precipitation_sum')
    })).reduceColumns(
        reducer=ee.Reducer.toList(3),
        selectors=['county_id', 'county_name', day_str]
    ).getInfo()['list']

    precip_df = pd.DataFrame(precip_data, columns=['county_id', 'county_name', day_str])
    precips_1 = pd.merge(precips_1, precip_df, on=['county_id', 'county_name'], how='left')

# Get county names and IDs
county_ids = wa_counties.aggregate_array('GEOID').getInfo()
county_names = wa_counties.aggregate_array('NAME').getInfo()

# Create base DataFrames with both ID and name
temps_daily1 = pd.DataFrame({'county_id': county_ids, 'county_name': county_names})
precips_daily1 = pd.DataFrame({'county_id': county_ids, 'county_name': county_names})

# Loop over each day in 2024 (example: DOY 121–123)
for doy in range(121, 124): #182
    date = datetime.date(2024, 1, 1) + datetime.timedelta(days=doy - 1) 

    # Use DOY as string for column name
    doy_str = str(doy)

    # Get the image for the day
    img = era5_2024.filterDate(date.strftime('%Y-%m-%d')).first()

    if img is None:
        print(f"No image found for DOY {doy}")
        continue

    # Reduce to mean per county
    reduced = img.reduceRegions(
        collection=wa_counties,
        reducer=ee.Reducer.mean(),
        scale=10000
    )

    # Add county metadata
    def add_county_info(f):
        return f.set({
            'county_id': f.get('GEOID'),
            'county_name': f.get('NAME')
        })

    reduced = reduced.map(add_county_info)

    # ------- TEMPERATURE --------
    temp_data = reduced.map(lambda f: ee.Feature(None, {
        'county_id': f.get('county_id'),
        'county_name': f.get('county_name'),
        doy_str: f.get('temperature_2m')
    })).reduceColumns(
        reducer=ee.Reducer.toList(3),
        selectors=['county_id', 'county_name', doy_str]
    ).getInfo()['list']

    temp_df = pd.DataFrame(temp_data, columns=['county_id', 'county_name', doy_str])
    temps_daily1 = pd.merge(temps_daily1, temp_df, on=['county_id', 'county_name'], how='left')

    # ------- PRECIPITATION --------
    precip_data = reduced.map(lambda f: ee.Feature(None, {
        'county_id': f.get('county_id'),
        'county_name': f.get('county_name'),
        doy_str: f.get('total_precipitation_sum')
    })).reduceColumns(
        reducer=ee.Reducer.toList(3),
        selectors=['county_id', 'county_name', doy_str]
    ).getInfo()['list']

    precip_df = pd.DataFrame(precip_data, columns=['county_id', 'county_name', doy_str])
    precips_daily1 = pd.merge(precips_daily1, precip_df, on=['county_id', 'county_name'], how='left')

Anomalies

# Ensure both have same index and columns
obs = temps_daily1.set_index('county_id').copy()
clim = temps_1.set_index('county_id').copy()

# Extract day columns (e.g., '121'–'365')
day_cols = [col for col in obs.columns if col.isdigit() and col in clim.columns]

# Subset and align
obs_days = obs[day_cols].astype(float).sort_index()
clim_days = clim[day_cols].astype(float).sort_index()

# Subtract climatology from observed: get daily anomalies
temps_anomalies = obs_days - clim_days

temps_anomalies.head()

	121	122	123
county_id
53001	-5.497249	-4.624398	-3.870438
53003	-5.542081	-5.544621	-4.568444
53005	-5.531265	-4.904690	-3.798700
53007	-4.404568	-4.427515	-2.366732
53009	-2.998318	-2.067569	-1.218782

# Ensure both have same index and columns
obs = precips_daily1.set_index('county_id').copy()
clim = precips_1.set_index('county_id').copy()

# Extract day columns (e.g., '121'–'365')
day_cols = [col for col in obs.columns if col.isdigit() and col in clim.columns]

# Subset and align
obs_days = obs[day_cols].astype(float).sort_index()
clim_days = clim[day_cols].astype(float).sort_index()

# Subtract climatology from observed: get daily anomalies
precips_anomalies = obs_days - clim_days

precips_anomalies.head()

	121	122	123
county_id
53001	-0.000599	-0.000334	-0.000397
53003	0.000802	0.001983	-0.001771
53005	-0.000170	-0.000247	-0.000128
53007	0.000179	0.000402	-0.002110
53009	0.003841	-0.002358	-0.005998

# List of anomaly DataFrames and their labels
anomalies_df = [
    ("Temperature", temps_anomalies),
    ("Precipitation", precips_anomalies)
]

# Create subplots: one for each variable
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 8), sharex=True)

for ax, (label, anomalies) in zip(axes, anomalies_df):
    # Identify day-of-year columns
    day_cols = [col for col in anomalies.columns if col.isdigit()]

    # Compute mean anomaly across counties for each day
    daily_mean_anomaly = anomalies[day_cols].mean(axis=0)

    # Reset index to long format
    daily_anomaly_long = daily_mean_anomaly.reset_index()
    daily_anomaly_long.columns = ['day', 'anomaly']
    daily_anomaly_long['day'] = daily_anomaly_long['day'].astype(int)
    daily_anomaly_long = daily_anomaly_long.sort_values("day")

    # Extract data
    x = daily_anomaly_long['day'].values
    y = daily_anomaly_long['anomaly'].values
    colors = ['red' if val < 0 else 'blue' for val in y]

    # Plot line and colored markers
    ax.plot(x, y, color='gray', linewidth=1.5, label=f'{label} Anomaly')
    ax.scatter(x, y, c=colors, edgecolor='black', s=40, zorder=3)
    ax.axhline(0, color='black', linestyle='--', linewidth=1)

    # Plot labels
    ax.set_title(f"Mean Daily {label} Anomalies Across All Counties (2024)")
    ax.set_ylabel("Anomaly")
    ax.grid(True)

# Common x-axis label
axes[-1].set_xlabel("Day of Year")

plt.tight_layout()
plt.show()

precips_anomalies['avg_anomaly'] = precips_anomalies[day_cols].mean(axis=1)
temps_anomalies['avg_anomaly'] = temps_anomalies[day_cols].mean(axis=1)

precips_anomaly_result = precips_anomalies[['avg_anomaly']].copy()
precips_anomaly_result.index.name = 'county_id'

temps_anomaly_result = temps_anomalies[['avg_anomaly']].copy()
temps_anomaly_result.index.name = 'county_id'


temps_anomaly_result.head()

	avg_anomaly
county_id
53001	-4.664028
53003	-5.218382
53005	-4.744885
53007	-3.732938
53009	-2.094890

# Step 1: Create EE dictionaries from each anomaly DataFrame
temps_anomaly_dict = ee.Dictionary(temps_anomaly_result['avg_anomaly'].to_dict())
precips_anomaly_dict = ee.Dictionary(precips_anomaly_result['avg_anomaly'].to_dict())

# Step 2: Define a function to set both anomalies to each county feature
def add_both_anomalies(feature):
    geoid = feature.get('GEOID')
    temp_val = temps_anomaly_dict.get(geoid)
    precip_val = precips_anomaly_dict.get(geoid)
    return feature.set({
        'avg_temp_anomaly': temp_val,
        'avg_precip_anomaly': precip_val
    })

# Step 3: Apply to the FeatureCollection
wa_counties_anomaly = wa_counties.map(add_both_anomalies)

# View the first feature (prints a dictionary of properties including avg_anomaly)
print(wa_counties_anomaly.first().getInfo())

{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-123.03842505513434, 47.52022697671061], [-123.03712338681052, 47.52022694173501], [-123.03649500317054, 47.52022694515167], [-123.03595643772823, 47.520226964995516], [-123.03546280327656, 47.52027182732147], [-123.03492416276187, 47.52027178186103], [-123.03478950261062, 47.520271853946085], [-123.03317374380099, 47.52027181311768], [-123.0320067111646, 47.520271817077706], [-123.02989721232956, 47.52027180022522], [-123.02801207378172, 47.52027182594219], [-123.02747352822992, 47.520271793599555], [-123.02689006454061, 47.52027178900823], [-123.02635149571962, 47.520271842063124], [-123.02585766573985, 47.52031671709755], [-123.0247356401448, 47.52031674486688], [-123.02316471624272, 47.520316707272535], [-123.02285062014514, 47.52031671842357], [-123.02231198353363, 47.520316687659246], [-123.02069613247596, 47.52031672675315], [-123.01957402990368, 47.52031672168074], [-123.0191701289741, 47.52031668187846], [-123.01782365155627, 47.520316722896105], [-123.0171054598988, 47.52031673219281], [-123.01571410370869, 47.52031667448163], [-123.01535499798868, 47.52031671736121], [-123.01504091289458, 47.52031670048769], [-123.01463691723552, 47.52031667308499], [-123.0139636267253, 47.52031672412305], [-123.01360462677366, 47.520316726504866], [-123.01320063371931, 47.520316692472974], [-123.01275180169262, 47.52031673936927], [-123.01239272675171, 47.52031667779809], [-123.01118091026429, 47.520316671295824], [-123.009071347284, 47.52031673967816], [-123.00866743939, 47.52031674370936], [-123.00803908260632, 47.52031672746135], [-123.00512164276857, 47.52031673063382], [-123.00296724896042, 47.52027178993289], [-123.00126171025268, 47.52027184362775], [-122.99901747596427, 47.52027181556992], [-122.99636943004647, 47.5202718285406], [-122.99417010586866, 47.52027184538572], [-122.99363148718054, 47.52027182271681], [-122.99340709271067, 47.520316707785724], [-122.99237481073507, 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[-122.96813780055882, 47.52152859589944], [-122.96777873514998, 47.52152853607277], [-122.96750946185563, 47.521528565892616], [-122.9671953179637, 47.52157344567276], [-122.96683615072533, 47.52157341524914], [-122.96652196960298, 47.52157344228745], [-122.96625269754192, 47.521573468246196], [-122.96589371166031, 47.52157345971966], [-122.96562436349983, 47.521573424719485], [-122.96531011693192, 47.52161833107506], [-122.96495113218825, 47.52161831868943], [-122.96459204505607, 47.52161833809175], [-122.96423295850465, 47.521618356021115], [-122.96387397550382, 47.52161833921824], [-122.96360462950626, 47.52161829800826], [-122.96329038327686, 47.52166319719529], [-122.96297620649507, 47.52166321151892], [-122.96266203015794, 47.521663224715006], [-122.96234785426557, 47.52166323678352], [-122.96198887392855, 47.521663212256506], [-122.9615848834242, 47.52166321709603], [-122.9601485888592, 47.521708081239865], [-122.95992415816175, 47.521752974779986], [-122.95880201211813, 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[-123.00969971512343, 47.551734997337086], [-123.01005879205532, 47.55142082791215], [-123.01055254159185, 47.55097201960808], [-123.01329044909075, 47.5483687608829], [-123.01607318998504, 47.54567574944855], [-123.02285054523453, 47.53925741784454], [-123.0234339980812, 47.5387188556851], [-123.02388284400315, 47.538270021894895], [-123.0246459109948, 47.53755188011327], [-123.02469074989592, 47.53750698609824], [-123.02527426427434, 47.53692350160446], [-123.02576790712868, 47.53647467790432], [-123.02653099221224, 47.53571171036687], [-123.02684519441972, 47.535442404535154], [-123.02742857528145, 47.534903766710485], [-123.02783261269374, 47.53449980788252], [-123.02837121477714, 47.53400612573372], [-123.02895473785216, 47.53342262634014], [-123.02931373932712, 47.53310843259257], [-123.03272482900368, 47.528305938959825], [-123.03272482392931, 47.528261050372365], [-123.03303909356598, 47.52781223717755], [-123.03842505513434, 47.52022697671061]]]}, 'id': '00000000000000000030', 'properties': {'ALAND': 1023381117, 'AWATER': 442114809, 'CBSAFP': '14740', 'CLASSFP': 'H1', 'COUNTYFP': '035', 'COUNTYNS': '01529223', 'CSAFP': '500', 'FUNCSTAT': 'A', 'GEOID': '53035', 'INTPTLAT': '+47.6396874', 'INTPTLON': '-122.6496358', 'LSAD': '06', 'METDIVFP': '', 'MTFCC': 'G4020', 'NAME': 'Kitsap', 'NAMELSAD': 'Kitsap County', 'STATEFP': '53', 'avg_precip_anomaly': -0.0003468792588634216, 'avg_temp_anomaly': -2.876979509421176}}

# Convert FeatureCollection to GeoJSON
geojson = wa_counties_anomaly.getInfo()

# Extract temperature anomaly values
temp_vals = [f['properties'].get('avg_temp_anomaly') for f in geojson['features'] if f['properties'].get('avg_temp_anomaly') is not None]
temp_min, temp_max = min(temp_vals), max(temp_vals)

# Create colormap for temperature
temp_colormap = cm.LinearColormap(['blue', 'white', 'red'], vmin=temp_min, vmax=temp_max)
temp_colormap.caption = 'Avg Temperature Anomaly (°C)'

# Create folium map for temperature
Map_temp = folium.Map(location=[47.5, -120], zoom_start=6)
temp_colormap.add_to(Map_temp)

# Style function for temperature
def temp_style(feature):
    val = feature['properties'].get('avg_temp_anomaly')
    color = 'gray' if val is None else temp_colormap(val)
    return {
        'fillOpacity': 0.7,
        'weight': 1,
        'color': 'black',
        'fillColor': color
    }

# Add layer
folium.GeoJson(
    geojson,
    name='Temperature Anomaly',
    style_function=temp_style,
    tooltip=folium.GeoJsonTooltip(fields=['avg_temp_anomaly'], aliases=['Temp Anomaly (°C):'])
).add_to(Map_temp)

folium.LayerControl().add_to(Map_temp)
Map_temp

Make this Notebook Trusted to load map: File -> Trust Notebook

# Extract precipitation anomaly values
precip_vals = [f['properties'].get('avg_precip_anomaly') for f in geojson['features'] if f['properties'].get('avg_precip_anomaly') is not None]
precip_min, precip_max = min(precip_vals), max(precip_vals)

# Create colormap for precipitation
precip_colormap = cm.LinearColormap(['brown', 'white', 'green'], vmin=precip_min, vmax=precip_max)
precip_colormap.caption = 'Avg Precipitation Anomaly (mm)'

# Create folium map for precipitation
Map_precip = folium.Map(location=[47.5, -120], zoom_start=6)
precip_colormap.add_to(Map_precip)

# Style function for precipitation
def precip_style(feature):
    val = feature['properties'].get('avg_precip_anomaly')
    color = 'gray' if val is None else precip_colormap(val)
    return {
        'fillOpacity': 0.7,
        'weight': 1,
        'color': 'black',
        'fillColor': color
    }

# Add layer
folium.GeoJson(
    geojson,
    name='Precipitation Anomaly',
    style_function=precip_style,
    tooltip=folium.GeoJsonTooltip(fields=['avg_precip_anomaly'], aliases=['Precip Anomaly (mm):'])
).add_to(Map_precip)

folium.LayerControl().add_to(Map_precip)
Map_precip

Make this Notebook Trusted to load map: File -> Trust Notebook

# Convert EE FeatureCollection to GeoJSON
geojson = wa_counties_anomaly.getInfo()  # Only safe for small datasets

# Create base folium map
Map = folium.Map(location=[47.5, -120], zoom_start=6)

# -------------------- TEMPERATURE ANOMALY LAYER --------------------

# Extract temperature anomaly values
temp_vals = [f['properties'].get('avg_temp_anomaly') for f in geojson['features'] if f['properties'].get('avg_temp_anomaly') is not None]
temp_min, temp_max = min(temp_vals), max(temp_vals)

# Create colormap for temperature
temp_colormap = cm.LinearColormap(['#32CD32', 'gray', 'purple'], vmin=temp_min, vmax=temp_max)
temp_colormap.caption = 'Avg Temperature Anomaly (°C)'
temp_colormap.add_to(Map)

# Define style function for temperature
def temp_style(feature):
    val = feature['properties'].get('avg_temp_anomaly')
    color = 'gray' if val is None else temp_colormap(val)
    return {
        'fillOpacity': 0.7,
        'weight': 1,
        'color': 'black',
        'fillColor': color
    }

# Add temp layer
folium.GeoJson(
    geojson,
    name='Temperature Anomaly',
    style_function=temp_style,
    tooltip=folium.GeoJsonTooltip(fields=['avg_temp_anomaly'], aliases=['Temp Anomaly (°C):'])
).add_to(Map)


# -------------------- PRECIPITATION ANOMALY LAYER --------------------

# Extract precipitation anomaly values
precip_vals = [f['properties'].get('avg_precip_anomaly') for f in geojson['features'] if f['properties'].get('avg_precip_anomaly') is not None]
precip_min, precip_max = min(precip_vals), max(precip_vals)

# Create colormap for precipitation
precip_colormap = cm.LinearColormap(['coral', 'gray',  '#0571b0'], vmin=precip_min, vmax=precip_max)
precip_colormap.caption = 'Avg Precipitation Anomaly (mm)'
precip_colormap.add_to(Map)

# Define style function for precipitation
def precip_style(feature):
    val = feature['properties'].get('avg_precip_anomaly')
    color = 'gray' if val is None else precip_colormap(val)
    return {
        'fillOpacity': 0.7,
        'weight': 1,
        'color': 'black',
        'fillColor': color
    }

# Add precip layer
folium.GeoJson(
    geojson,
    name='Precipitation Anomaly',
    style_function=precip_style,
    tooltip=folium.GeoJsonTooltip(fields=['avg_precip_anomaly'], aliases=['Precip Anomaly (mm):'])
).add_to(Map)


# -------------------- FINAL MAP SETUP --------------------

folium.LayerControl().add_to(Map)
Map

Make this Notebook Trusted to load map: File -> Trust Notebook

This is the end of the script

Thank you.