How to Access NASA’s Climate Data — And How It’s Powering the Fight Against Climate Change Pt. 1

can’t consider a extra vital dataset. Simply at the moment, I noticed a headline like this: ‘Warmth Waves Are Getting Extra Harmful with Local weather Change.’ You possibly can’t say we haven’t been warned. In 1988, we noticed headlines like this: ‘World Warming Has Begun, Skilled Tells Senate.’ And whereas information science has performed its position in revealing that we are going to doubtless surpass the 1.5 °C goal set by the Paris Settlement, there may be much more we may very well be doing. For one, folks don’t imagine it, but the information is available, free, and simple to entry. You possibly can test it your self! So on this episode, we are going to. We’ll additionally discuss concerning the shocking and attention-grabbing methods this information is at present being utilized to fight the results of local weather change. 

However local weather information can be extremely attention-grabbing. You’ve most likely additionally seen headlines like: Blue Origin launch of 6 folks to suborbital area delayed once more resulting from climate. Which makes you assume, if we will ship somebody to the moon, then why can’t we ensure concerning the climate? If troublesome doesn’t describe it, then a multidimensional stochastic course of would possibly. From a knowledge science perspective, that is our Riemann Speculation, our P vs NP downside. How effectively we will mannequin and perceive local weather information will form our subsequent many years on this earth. That is a very powerful downside we may very well be engaged on. 

And whereas New York simply went by a warmth wave, it’s vital to notice that local weather change is worse than simply hotter climate. 

• Failing harvests undermine international meals safety, particularly in weak areas.
• Vector-borne illnesses broaden into new areas as temperatures rise.
• Mass extinctions disrupt ecosystems and erode planetary resilience.
• Ocean acidification unravels marine meals chains, threatening fisheries and biodiversity.
• Freshwater provides dwindle underneath the strain of drought, air pollution, and overuse.

However not all is misplaced; we are going to speak about among the methods information has been used to handle these issues. Right here’s a abstract of among the information NASA retains observe of. We are going to entry a few of these parameters.

Getting the information

We’ll begin by selecting some attention-grabbing areas we’ll study on this collection. All we’d like are their coordinates — a click on away on Google Maps. I take advantage of fairly a little bit of decimal locations right here, however the meteorological information supply decision is ½° x ⅝°, so there’s no have to be this correct. 

interesting_climate_sites = {
    "Barrow, Alaska (Utqiaġvik)": (71.2906, -156.7886),    # Arctic warming, permafrost soften
    "Greenland Ice Sheet": (72.0000, -40.0000),            # Glacial soften, sea stage rise
    "Amazon Rainforest (Manaus)": (-3.1190, -60.0217),     # Carbon sink, deforestation impression
    "Sahara Desert (Tamanrasset, Algeria)": (22.7850, 5.5228),  # Warmth extremes, desertification
    "Sahel (Niamey, Niger)": (13.5128, 2.1127),            # Precipitation shifts, droughts
    "Sydney, Australia": (-33.8688, 151.2093),             # Heatwaves, bushfires, El Niño sensitivity
    "Mumbai, India": (19.0760, 72.8777),                   # Monsoon variability, coastal flooding
    "Bangkok, Thailand": (13.7563, 100.5018),              # Sea-level rise, warmth + humidity
    "Svalbard, Norway": (78.2232, 15.6469),                # Quickest Arctic warming
    "McMurdo Station, Antarctica": (-77.8419, 166.6863),   # Ice loss, ozone gap proximity
    "Cape City, South Africa": (-33.9249, 18.4241),        # Water shortage, shifting rainfall
    "Mexico Metropolis, Mexico": (19.4326, -99.1332),            # Air air pollution, altitude-driven climate
    "Reykjavík, Iceland": (64.1355, -21.8954),             # Glacial soften, geothermal dynamics
}

Subsequent, let’s choose some parameters. You possibly can flip by them within the Parameter Dictionary https://power.larc.nasa.gov/parameters/

You possibly can solely request from one neighborhood at a time, so we group the parameters by neighborhood.

community_params = {
    "AG": ["T2M","T2M_MAX","T2M_MIN","WS2M","ALLSKY_SFC_SW_DWN","ALLSKY_SFC_LW_DWN",
           "CLRSKY_SFC_SW_DWN","T2MDEW","T2MWET","PS","RAIN","TS","RH2M","QV2M","CLOUD_AMT"],
    "RE": ["WD2M","WD50M","WS50M"],
    "SB": ["IMERG_PRECTOT"]
}

How is that this information used?

• AG = Agricultural. Agroeconomists usually use this neighborhood in crop development fashions, akin to DSSAT and APSIM, in addition to in irrigation planners like FAO CROPWAT. It’s additionally used for livestock warmth stress evaluation and in constructing meals safety early warning methods. This helps mitigate meals insecurity resulting from local weather change. This information follows agroeconomic conventions, permitting it to be ingested immediately by agricultural decision-support instruments.
• RE = Renewable Vitality. Given the identify and the truth that you will get windspeed information from right here, you would possibly be capable to guess its use. This information is primarily used to forecast long-term vitality yields. Wind velocity for generators, photo voltaic radiation for photo voltaic farms. This information will be fed into PVsyst, NREL-SAM and WindPRO to estimate annual vitality yields and prices. This information helps the whole lot from rooftop array design to nationwide clear vitality targets.
• SB = Sustainable Buildings. Architects and HVAC engineers make the most of this information to make sure their buildings adjust to vitality efficiency laws, like IECC or ASHRAE 90.1. It may be immediately dropped into EnergyPlus, OpenStudio, RETScreen, or LEED/ASHRAE compliance calculators to confirm buildings are as much as code.

Now we choose a begin and finish date. 

start_date = "19810101"
end_date   = "20241231"

To make the API name one thing repeatable, we use a perform. We are going to work with each day information, however if you happen to choose yearly, month-to-month, and even hourly information, you simply want to alter the URL to 

…/temporal/{decision}/level.

import requests
import pandas as pd

def get_nasa_power_data(lat, lon, parameters, neighborhood, begin, finish):
    """
    Fetch each day information from NASA POWER API for given parameters and placement.
    Dates have to be in YYYYMMDD format (e.g., "20100101", "20201231").
    """
    url = "https://energy.larc.nasa.gov/api/temporal/each day/level"
    params = {
        "parameters": ",".be a part of(parameters),
        "neighborhood": neighborhood,
        "latitude": lat,
        "longitude": lon,
        "begin": begin,
        "finish": finish,
        "format": "JSON"
    }
    response = requests.get(url, params=params)
    information = response.json()

    if "properties" not in information:
        print(f"Error fetching {neighborhood} information for lat={lat}, lon={lon}: {information}")
        return pd.DataFrame()

    # Construct one DataFrame per parameter, then mix
    param_data = information["properties"]["parameter"]
    dfs = [
        pd.DataFrame.from_dict(values, orient="index", columns=[param])
        for param, values in param_data.gadgets()
    ]
    df_combined = pd.concat(dfs, axis=1)
    df_combined.index.identify = "Date"
    return df_combined.sort_index().astype(float)

This perform retrieves the parameters we requested from the neighborhood we specified. It additionally converts JSON right into a dataframe. Every response at all times comprises a property key — if it’s lacking, we print an error.

Let’s name this perform in a loop to fetch the information for all our areas. 

all_data = {}
for metropolis, (lat, lon) in interesting_climate_sites.gadgets():
    print(f"Fetching each day information for {metropolis}...")
    city_data = {}
    for neighborhood, params in community_params.gadgets():
        df = get_nasa_power_data(lat, lon, params, neighborhood, start_date, end_date)
        city_data[community] = df
    all_data[city] = city_data

Proper now, our information is a dictionary the place the values are additionally dictionaries. It seems to be like this:

This makes utilizing the information sophisticated. Subsequent, we mix these into one dataframe. We be a part of on the information after which concatenate. Since there have been no lacking values, an internal be a part of would yield the identical end result. 

# 1) For every metropolis, be a part of its communities on the date index
city_dfs = {
    metropolis: comms["AG"]
                .be a part of(comms["RE"], how="outer")
                .be a part of(comms["SB"], how="outer")
    for metropolis, comms in all_data.gadgets()
}

# 2) Concatenate into one MultiIndexed DF: index = (Metropolis, Date)
combined_df = pd.concat(city_dfs, names=["City", "Date"])

# 3) Reset the index so Metropolis and Date grow to be columns
combined_df = combined_df.reset_index()

# 4) Deliver latitude/longitude in as columns
coords = pd.DataFrame.from_dict(
    interesting_climate_sites, orient="index", columns=["Latitude", "Longitude"]
).reset_index().rename(columns={"index": "Metropolis"})

combined_df = combined_df.merge(coords, on="Metropolis", how="left")

# then save into your Drive folder
combined_df.to_csv('/content material/drive/MyDrive/climate_data.csv', index=False)

Should you’re bored with coding for the day, you can too use their information entry instrument. Simply click on anyplace on the map to retrieve the information. Right here I clicked on Venice. Then simply choose a Neighborhood, Temporal Common, and your most well-liked file kind, CSV, JSON, ASCII, NETCDF, and hit submit. A few clicks and you will get all of the climate information on this planet. 

https://power.larc.nasa.gov/data-access-viewer

Sanity test

Now, let’s carry out a fast sanity test to confirm that the information we now have is smart.  

import matplotlib.pyplot as plt
import seaborn as sns # Import seaborn

# Load information
climate_df = pd.read_csv('/content material/drive/MyDrive/TDS/Local weather/climate_data.csv')
climate_df['Date'] = pd.to_datetime(climate_df['Date'].astype(str), format='%Ypercentmpercentd')

# Filter for the desired cities
selected_cities = [
    'McMurdo Station, Antarctica',
    'Bangkok, Thailand',
]
df_selected_cities = climate_df[climate_df['City'].isin(selected_cities)].copy()

# Create a scatter plot with completely different colours for every metropolis
plt.determine(figsize=(12, 8))

# Use a colormap for extra aesthetic colours
colours = sns.color_palette("Set2", len(selected_cities)) # Utilizing a seaborn shade palette

for i, metropolis in enumerate(selected_cities):
    df_city = df_selected_cities[df_selected_cities['City'] == metropolis]
    plt.scatter(df_city['Date'], df_city['T2M'], label=metropolis, s=2, shade=colours[i]) # Utilizing T2M for temperature and smaller dots

plt.xlabel('Date')
plt.ylabel('Temperature (°C)')
plt.title('Day by day Temperature (°C) for Chosen Cities')
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.present()

Sure, temperatures in Bangkok are fairly a bit hotter than within the Arctic.

# Filter for the desired cities
selected_cities = [
    'Cape Town, South Africa',
    'Amazon Rainforest (Manaus)',
]
df_selected_cities = climate_df[climate_df['City'].isin(selected_cities)].copy()

# Arrange the colour palette
colours = sns.color_palette("Set1", len(selected_cities))

# Create vertically stacked subplots
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 10), sharex=True)

for i, metropolis in enumerate(selected_cities):
    df_city = df_selected_cities[df_selected_cities['City'] == metropolis]
    axes[i].scatter(df_city['Date'], df_city['PRECTOTCORR'], s=2, shade=colours[i])
    axes[i].set_title(f'Day by day Precipitation in {metropolis}')
    axes[i].set_ylabel('Precipitation (mm)')
    axes[i].grid(alpha=0.3)

# Label x-axis solely on the underside subplot
axes[-1].set_xlabel('Date')

plt.tight_layout()
plt.present()

Sure, it’s raining extra within the Amazon Rainforest than in South Africa. 

South Africa experiences droughts, which place a major burden on the agricultural sector. 

# Filter for Mexico Metropolis
df_mexico = climate_df[climate_df['City'] == 'Mexico Metropolis, Mexico'].copy()

# Create the plot
plt.determine(figsize=(12, 6))
sns.set_palette("husl")

plt.scatter(df_mexico['Date'], df_mexico['WS2M'], s=2, label='WS2M (2m Wind Pace)')
plt.scatter(df_mexico['Date'], df_mexico['WS50M'], s=2, label='WS50M (50m Wind Pace)')

plt.xlabel('Date')
plt.ylabel('Wind Pace (m/s)')
plt.title('Day by day Wind Speeds at 2m and 50m in Mexico Metropolis')
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.present()

Sure, wind speeds at 50 meters are lots sooner than at 2 meters. 

Usually, the upper you go, the sooner the wind strikes. At flight altitude, the wind can attain speeds of 200 km/h. That’s, till you attain area at 100,000 meters. 

We’ll take a a lot nearer have a look at this information within the following chapters.

It’s heating up

We simply went by a warmth wave right here in Toronto. By the sounds my AC made, I believe it practically broke. However in a temperature graph, you could look fairly fastidiously to see that they’re rising. It is because there may be seasonality and vital variability. Issues grow to be clearer after we have a look at the yearly common. We name an anomaly the distinction between the typical for a selected 12 months and the baseline. The baseline being the typical temperature over 1981–2024, we will then see that the current yearly common is considerably larger than the baseline, primarily because of the cooler temperatures current in earlier years. The converse is equally true; The early yearly common is considerably decrease than the baseline resulting from hotter temperatures lately. 

With all of the technical articles current right here, headlines like ‘Grammar as an Injectable: A Trojan Horse to NLP Pure Language Processing’. I hope you’re not disillusioned by a easy linear regression. However that’s all it takes to point out that temperatures are rising. But folks don’t imagine. 

# 1) Filter for Sahara Desert and exclude 2024
metropolis = 'Sahara Desert (Tamanrasset, Algeria)'
df = (
    climate_df
    .loc[climate_df['City'] == metropolis]
    .set_index('Date')
    .sort_index()
)

# 2) Compute annual imply & anomaly
annual = df['T2M'].resample('Y').imply()
baseline = annual.imply()
anomaly = annual - baseline

# 3) 5-year rolling imply
roll5 = anomaly.rolling(window=5, heart=True, min_periods=3).imply()

# 4) Linear pattern
years = anomaly.index.12 months
slope, intercept = np.polyfit(years, anomaly.values, 1)
pattern = slope * years + intercept

# 5) Plot
plt.determine(figsize=(10, 6))
plt.bar(years, anomaly, shade='lightgray', label='Annual Anomaly')
plt.plot(years, roll5, shade='C0', linewidth=2, label='5-yr Rolling Imply')
plt.plot(years, pattern, shade='C3', linestyle='--', linewidth=2,
         label=f'Pattern: {slope:.3f}°C/yr')
plt.axhline(0, shade='ok', linewidth=0.8, alpha=0.6)

plt.xlabel('Yr')
plt.ylabel('Temperature Anomaly (°C)')
plt.title(f'{metropolis} Annual Temperature Anomaly')
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.present()

The Sahara is getting hotter by 0.03°C per 12 months. That’s the most well liked desert on this planet. We are able to even test each location we picked and see that not a single one has a destructive pattern.

So sure, Temperatures are rising. 

The forest for the timber

A giant motive NASA makes this information open-source is to fight the results of Local weather Change. We’ve talked about modelling crop yields, renewable vitality, and sustainable constructing compliance. Nevertheless, there are extra methods information will be utilized to handle local weather change in a scientific and mathematically grounded method. Should you’re on this subject, this video by Luis Seco covers issues I didn’t get to handle on this article, like

• The carbon commerce and the value of carbon
• Predictive biomass instrument optimizing tree planting
• Protected ingesting water in Kenya 
• The socioeconomic prices of emissions
• Managed burning of forests

I hope you’ll be a part of me on this journey. Within the subsequent episode, we are going to focus on how differential equations have been used to mannequin local weather. And whereas a lot is being performed to handle local weather change, the sooner listing of results was not exhaustive. 

• Melting ice sheets destabilize international local weather regulation and speed up sea-level rise.
• Local weather-related damages cripple economies by escalating infrastructure and well being prices.
• Rising numbers of local weather refugees pressure borders and gasoline geopolitical instability.
• Coastal cities face submersion as seas rise relentlessly
• Excessive climate occasions shatter information, displacing tens of millions.

However there’s noise, and there’s sign, and they are often separated. 

Sources

• Local weather change impacts | Nationwide Oceanic and Atmospheric Administration. (n.d.). https://www.noaa.gov/schooling/resource-collections/local weather/climate-change-impacts
• Freedman, A. (2025, June 23). Warmth waves are getting extra harmful with local weather change – and we should be underestimating them. CNN. https://www.cnn.com/2025/06/23/local weather/heat-wave-global-warming-links
• World local weather predictions present temperatures anticipated to stay at or close to file ranges in coming 5 years. World Meteorological Group. (2025, Might 26). https://wmo.int/information/media-centre/global-climate-predictions-show-temperatures-expected-remain-or-near-record-levels-coming-5-years
• World warming has begun, skilled tells Senate (revealed 1988). The New York Occasions. (1988, June 24). https://net.archive.org/net/20201202103915/https:/www.nytimes.com/1988/06/24/us/global-warming-has-begun-expert-tells-senate.html
• NASA. (n.d.). NASA LARC POWER Undertaking. NASA. https://energy.larc.nasa.gov/
• Wall, M. (2025, June 20). Blue Origin to launch 6 folks to Suborbital Area June 29 after climate delay. Area. https://www.area.com/space-exploration/private-spaceflight/watch-blue-origin-launch-6-people-to-suborbital-space-on-june-21

Code Available Here

Linkedin