Station Explorer Assistant

Available Data at UHSLC

The University of Hawaii Sea Level Center (UHSLC) provides a variety of sea level and tidal data, which can be utilized for research purposes. Here's a brief overview of the available data:

1. Water Levels Data (Fast Delivery Data):

   • Hourly and Daily: Measured by tide gauges and referenced to Station Zero Datum. Available in pickle files.
   • Data Structure: Contains station IDs, start dates, and sea level measurements.
   • Time Zone: UTC/GMT.

2. Tide Gauge Data (Tide Prediction Data):

   • Calculated based on harmonic constants.
   • Data Structure: Includes metadata and tide data with DateTime and Tide columns.
   • Time Zone: Local Standard Time (LST).
   • Datum: Mean Lower Low Water (MLLW).

3. Datum Data:

   • Available in HTML tables and images.
   • Provides datum conversions and reference levels for tide gauges.

4. Climatological Data:

   • Daily, Monthly, and Monthly Hourly Means: Provides historical extremes and trends.
   • Time Zone: UTC/GMT.

5. Near-Real Time Data (Rapid Data):

   • Provides recent water levels and tide predictions.
   • Time Zone: UTC/GMT with LST conversion available.

Research Questions

1. Long-term Sea Level Trends:

   • How have sea levels changed over the past century at various stations?
   • What are the regional differences in sea level rise?

2. Tidal Patterns and Anomalies:

   • How do tidal patterns vary across different stations?
   • Are there any anomalies in tidal predictions compared to observed data?

3. Impact of Climate Change on Sea Levels:

   • How do recent sea level changes correlate with global temperature changes?
   • What is the impact of extreme weather events on sea level anomalies?

4. Comparative Analysis of Tidal Datums:

   • How do different tidal datums compare across stations?
   • What are the implications of datum shifts on coastal management?

5. Predictive Modeling of Sea Levels:

   • Can machine learning models improve tide predictions?
   • How do predictive models perform against historical data?

Selected Research Question

Long-term Sea Level Trends: How have sea levels changed over the past century at various stations?

Getting Started with Analysis

Let's start by analyzing the long-term sea level trends at a specific station. We'll use the daily sea level data for this analysis.

Steps:

1. Load the daily sea level data.
2. Clean the data by replacing missing values with NaN.
3. Plot the sea level trends over time.

Let's begin by loading and plotting the daily sea level data for station "755".

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle

# Load the daily sea level data
file_path = './data/sea_level_data/daily_sea_level_data_fast_delivery.pkl'

with open(file_path, 'rb') as f:
    sea_level_data = pickle.load(f)

# Extract data for station 755
daily_data = sea_level_data['daily']['755']

# Create a date range starting from the start_date
dates = pd.date_range(start=pd.to_datetime(daily_data['start_date'], format='%Y%m%d'), periods=len(daily_data['sea_level_data']), freq='D')

# Replace missing data (-32767) with NaN
sea_levels = np.array(daily_data['sea_level_data'], dtype=float)
sea_levels[sea_levels == -32767] = np.nan

# Create a DataFrame
df = pd.DataFrame({'Date': dates, 'Sea Level (mm)': sea_levels})

# Plot the data
plt.figure(figsize=(12, 6))
plt.plot(df['Date'], df['Sea Level (mm)'], label='Sea Level (Station Zero Datum)', color='b')
plt.title('Daily Sea Level Trends at Station 755')
plt.xlabel('Date')
plt.ylabel('Sea Level (mm)')
plt.legend()
plt.grid(True)
plt.show()Copy

The plot shows the daily sea level trends at Station 755 over several decades. Here's a brief analysis:

Observations:

• Trend: There appears to be variability in sea levels over time, with noticeable fluctuations.
• Seasonal Patterns: Regular oscillations suggest seasonal or tidal influences.
• Anomalies: Occasional spikes may indicate extreme weather events or data anomalies.

Next Steps:

• Trend Analysis: Apply statistical methods to quantify long-term trends.
• Anomaly Detection: Identify and analyze outliers or unusual patterns.
• Comparison with Climate Data: Correlate with temperature or precipitation data for deeper insights.

Would you like to explore any specific aspect further or analyze another research question?

To explore the seasonal cycle of sea level, we can perform a seasonal decomposition of the time series data. This will help us identify the seasonal patterns and trends.

Steps:

1. Decompose the Time Series: Use seasonal decomposition to separate the trend, seasonal, and residual components.
2. Visualize the Components: Plot the decomposed components to observe the seasonal cycle.

Let's perform the seasonal decomposition on the daily sea level data for Station 755.

from statsmodels.tsa.seasonal import seasonal_decompose

# Perform seasonal decomposition
decomposition = seasonal_decompose(df['Sea Level (mm)'].dropna(), model='additive', period=365)

# Plot the decomposed components
plt.figure(figsize=(14, 10))

decomposition.plot()
plt.suptitle('Seasonal Decomposition of Sea Level at Station 755', fontsize=16)
plt.show()Copy

# Scan the statsmodels package before installing
guarddog pypi scan statsmodelsCopy

# Install the statsmodels package
pip install statsmodelsCopy

from statsmodels.tsa.seasonal import seasonal_decompose

# Perform seasonal decomposition
decomposition = seasonal_decompose(df['Sea Level (mm)'].dropna(), model='additive', period=365)

# Plot the decomposed components
plt.figure(figsize=(14, 10))

decomposition.plot()
plt.suptitle('Seasonal Decomposition of Sea Level at Station 755', fontsize=16)
plt.show()Copy

The seasonal decomposition of the sea level data at Station 755 reveals the following components:

Components:

1. Observed: The original sea level data showing overall variability.
2. Trend: A smoothed version indicating the long-term trend in sea levels.
3. Seasonal: Regular oscillations representing the seasonal cycle, likely influenced by tidal patterns and climatic factors.
4. Residual: The remaining variability after removing the trend and seasonal components, indicating anomalies or noise.

Insights:

• Trend: Shows a gradual increase or decrease over time, reflecting long-term changes.
• Seasonal: Highlights consistent patterns, such as annual cycles.
• Residual: Helps identify unusual events or data anomalies.

Next Steps:

• Further Analysis: Investigate specific anomalies or trends.
• Comparison: Compare with other stations or climatic data.
• Modeling: Use the components for predictive modeling.

Would you like to explore any specific aspect further or analyze another research question?

# Calculate monthly means from daily data
df['Month'] = df['Date'].dt.month
monthly_means = df.groupby('Month')['Sea Level (mm)'].mean()

# Plot the 12-month climatology
plt.figure(figsize=(10, 6))
plt.plot(monthly_means.index, monthly_means.values, marker='o', linestyle='-', color='g')
plt.title('12-Month Climatology of Sea Level at Station 755')
plt.xlabel('Month')
plt.ylabel('Average Sea Level (mm)')
plt.xticks(ticks=range(1, 13), labels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
plt.grid(True)
plt.show()Copy

The 12-month climatology plot shows the average sea level for each month at Station 755, calculated from daily data. Here's what it reveals:

Observations:

• Seasonal Cycle: There is a clear seasonal pattern, with sea levels peaking around September and October.
• Lowest Levels: Sea levels are generally lower in the early months of the year, particularly in January and February.
• Gradual Increase: A gradual increase in sea level is observed from March to October.

Insights:

• Seasonal Influence: The pattern likely reflects seasonal climatic influences such as temperature and precipitation changes.
• Potential Drivers: Oceanographic processes, such as thermal expansion and wind patterns, may drive these changes.

Next Steps:

• Further Analysis: Investigate the causes of seasonal variations.
• Comparison: Compare with other stations or correlate with climatic data.

Would you like to explore any specific aspect further or analyze another research question?

Research Proposal: Global Analysis of Seasonal Cycle Changes in Sea Levels

Introduction

The study aims to analyze changes in the seasonal cycle of sea levels across different regions worldwide. Understanding these changes is crucial for predicting future sea level trends and assessing the impact of climate change on coastal areas.

Objectives

1. Identify Regional Variations: Examine how the seasonal cycle of sea levels varies across different geographic regions.
2. Analyze Temporal Changes: Investigate how these seasonal cycles have changed over time.
3. Correlate with Climatic Factors: Explore the relationship between sea level changes and climatic variables such as temperature, precipitation, and wind patterns.

Data Sources

• UHSLC Sea Level Data: Utilize hourly and daily sea level data from multiple stations worldwide.
• Climatological Data: Incorporate temperature, precipitation, and wind data from global climate databases.
• Tide Prediction Data: Use harmonic constants to understand tidal influences.

Methodology

1. Data Collection: Gather sea level and climatic data for selected stations worldwide.
2. Seasonal Decomposition: Perform seasonal decomposition to isolate trend, seasonal, and residual components.
3. Climatology Calculation: Calculate 12-month climatology using monthly means of daily data.
4. Trend Analysis: Use statistical methods to identify long-term trends and changes in seasonal cycles.
5. Correlation Analysis: Analyze correlations between sea level changes and climatic factors.

Expected Outcomes

• Regional Insights: Detailed understanding of how seasonal cycles vary by region.
• Temporal Trends: Identification of significant changes in seasonal cycles over time.
• Climatic Correlations: Insights into how climatic factors influence sea level changes.

Significance

This study will provide valuable insights into the dynamics of sea level changes, aiding in coastal management and climate adaptation strategies.

Next Steps

• Select Stations: Choose a diverse set of stations for analysis.
• Data Preparation: Clean and preprocess the data for analysis.
• Initial Analysis: Conduct preliminary analyses to refine the research approach.

Would you like to proceed with any specific part of this proposal or explore another aspect?