diff --git a/chapter2/solutions/nyc_forecast_owm.py b/chapter2/solutions/nyc_forecast_owm.py
deleted file mode 100644
index 7a84079..0000000
--- a/chapter2/solutions/nyc_forecast_owm.py
+++ /dev/null
@@ -1,40 +0,0 @@
-'''
-Retrieve a day's forecast of New York City and create a graph
-Using https://github.com/csparpa/pyowm
-'''
-import matplotlib.pyplot as plt
-from pyowm import OWM
-import pytz
-
-owm = OWM('<token>')
-
-def get_forecast(city):
-    # this used to be daily_forecast
-    # https://github.com/csparpa/pyowm/issues/266
-    fc = owm.three_hours_forecast(city)
-    f = fc.get_forecast()
-    
-    # three_hours_forecast() returns 5 day forecast at a granularity
-    # of three hours
-    # We plot the forecast for the next 24 hours (so, first 8 intervals)
-    weathers = f.get_weathers()[0:8]
-    data_points = ['temp', 'temp_max', 'temp_min', 'temp_kf']
-    temp = []
-    date_time = []
-    for w in weathers:
-        forecast_temp = w.get_temperature('celsius')
-        utc_dt = datetime.utcfromtimestamp(w.get_reference_time()).replace(tzinfo=pytz.utc)
-        tz = pytz.timezone('America/New_York')
-        dt = utc_dt.astimezone(tz)
-        date_time.append(dt.strftime('%Y-%m-%d %H:%M'))
-        temp.append(forecast_temp['temp'])
-    x = range(1, len(temp)+1)
-    plt.plot(x, temp, 'o-')
-    plt.xticks(x, date_time, rotation=45)
-    plt.show()
-    # Uncomment below and comment the previous line to save the image
-    # plt.savefig('nyc_owm_forecast.png')
-
-
-if __name__ == '__main__':
-    get_forecast('new york, us')
diff --git a/chapter2/solutions/nyc_forecast_real.py b/chapter2/solutions/nyc_forecast_real.py
new file mode 100644
index 0000000..b4158df
--- /dev/null
+++ b/chapter2/solutions/nyc_forecast_real.py
@@ -0,0 +1,41 @@
+'''
+Retrieve a day's forecast of New York City and create a graph
+Using data downloaded from https://doingmathweatherforecast.ai.studio/
+'''
+import csv
+import matplotlib.pyplot as plt
+
+
+def read_csv(filename):
+
+    time_of_days = []
+    forecast_temperature = []
+
+    with open(filename) as f:
+        reader = csv.reader(f)
+        next(reader)
+        for row in reader:
+            # Extract only the time from date and time
+            time_of_day = row[0].split()[1]
+            time_of_days.append(time_of_day)
+            forecast_temperature.append(float(row[1]))
+
+    return time_of_days, forecast_temperature
+
+
+def plot_forecast(time_of_day, forecast_temperature):
+
+    # time_of_day = ['4 AM', '7 AM', '10 AM', '1 PM', '4 PM', '7PM', '10 PM']
+    # forecast_temp = [71, 70, 74, 80, 82, 81, 76]
+    time_interval = range(1, len(time_of_day) + 1)
+
+    plt.plot(time_interval, forecast_temperature, 'o-')
+    plt.xticks(time_interval, time_of_day)
+    plt.show()
+    # Uncomment below and comment the previous line to save the image
+    # plt.savefig('nyc_owm_forecast.png')
+
+
+if __name__ == '__main__':
+    time_of_day, forecast_temperature = read_csv("weather_New_York_1d_3h.csv")
+    plot_forecast(time_of_day, forecast_temperature)
diff --git a/chapter2/solutions/weather_New_York_1d_3h.csv b/chapter2/solutions/weather_New_York_1d_3h.csv
new file mode 100644
index 0000000..3bed0e6
--- /dev/null
+++ b/chapter2/solutions/weather_New_York_1d_3h.csv
@@ -0,0 +1,9 @@
+"Local Date & Time","Temperature"
+"2026-11-03 19:00","63.1"
+"2026-11-03 22:00","60.3"
+"2026-11-04 01:00","58.5"
+"2026-11-04 04:00","57.6"
+"2026-11-04 07:00","56.5"
+"2026-11-04 10:00","63.7"
+"2026-11-04 13:00","68.7"
+"2026-11-04 16:00","67.6"
\ No newline at end of file