Atmospheric Science Calculations

Usage

1. MCP Server Definition

import asyncio
import json
from contextlib import AsyncExitStack
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class AtmSciClient:
    """Atmospheric Science Tools MCP Client"""

    def __init__(self, server_url: str, api_key: str):
        self.server_url = server_url
        self.api_key = api_key
        self.session = None

    async def connect(self):
        print(f"Connecting to: {self.server_url}")
        try:
            self.transport = streamablehttp_client(
                url=self.server_url,
                headers={"SCP-HUB-API-KEY": self.api_key}
            )
            self._stack = AsyncExitStack()
            await self._stack.__aenter__()
            self.read, self.write, self.get_session_id = await self._stack.enter_async_context(self.transport)
            self.session_ctx = ClientSession(self.read, self.write)
            self.session = await self._stack.enter_async_context(self.session_ctx)
            await self.session.initialize()
            print("✓ connect success")
            return True
        except Exception as e:
            print(f"✗ connect failure: {e}")
            return False

    async def disconnect(self):
        """Disconnect from server"""
        try:
            if hasattr(self, '_stack'):
                await self._stack.aclose()
            print("✓ already disconnect")
        except Exception as e:
            print(f"✗ disconnect error: {e}")
    def parse_result(self, result):
        try:
            if hasattr(result, 'content') and result.content:
                content = result.content[0]
                if hasattr(content, 'text'):
                    return json.loads(content.text)
            return str(result)
        except Exception as e:
            return {"error": f"parse error: {e}", "raw": str(result)}

2. Atmospheric Calculations Workflow

Calculate key atmospheric parameters for meteorology, climate science, and weather forecasting applications.

Workflow Steps:

1. Calculate Coriolis Parameter - Compute Earth's rotation effect
2. Calculate Geostrophic Wind - Determine wind from pressure gradients
3. Calculate Heat Index - Assess human heat stress
4. Calculate Potential Temperature - Standardize temperature measurements
5. Calculate Dewpoint - Determine moisture content

Implementation:

## Initialize client
client = AtmSciClient(
    "https://scp.intern-ai.org.cn/api/v1/mcp/35/AtmSci-Tool",
    "<your-api-key>"
)

if not await client.connect():
    print("connection failed")
    exit()

print("=== Atmospheric Science Calculations ===\n")

## Step 1: Calculate Coriolis parameter
print("Step 1: Coriolis Parameter")
latitude = 45.0  # degrees
result = await client.session.call_tool(
    "atm_calc_coriolis_parameter",
    arguments={"latitude": latitude}
)
result_data = client.parse_result(result)
print(f"Latitude: {latitude}°")
print(f"Coriolis parameter: {result_data} s⁻¹\n")

## Step 2: Calculate geostrophic wind
print("Step 2: Geostrophic Wind")
result = await client.session.call_tool(
    "atm_calc_geostrophic_wind",
    arguments={
        "pressure_gradient_x": 1.0,  # Pa/m
        "pressure_gradient_y": 0.5,  # Pa/m
        "latitude": latitude,
        "air_density": 1.225         # kg/m³
    }
)
result_data = client.parse_result(result)
print(f"Geostrophic wind (u, v): {result_data} m/s\n")

## Step 3: Calculate heat index
print("Step 3: Heat Index")
result = await client.session.call_tool(
    "atm_calc_heat_index",
    arguments={
        "temperature_f": 95.0,       # °F
        "relative_humidity": 65.0    # %
    }
)
result_data = client.parse_result(result)
print(f"Temperature: 95°F, Humidity: 65%")
print(f"Heat index: {result_data}°F\n")

## Step 4: Calculate potential temperature
print("Step 4: Potential Temperature")
result = await client.session.call_tool(
    "atm_calc_potential_temperature",
    arguments={
        "temperature_k": 288.15,     # K (15°C)
        "pressure_pa": 85000.0       # Pa (850 hPa)
    }
)
result_data = client.parse_result(result)
print(f"Temperature: 288.15 K, Pressure: 850 hPa")
print(f"Potential temperature: {result_data} K\n")

## Step 5: Calculate dewpoint
print("Step 5: Dewpoint Temperature")
result = await client.session.call_tool(
    "atm_calc_dewpoint",
    arguments={
        "temperature_c": 25.0,       # °C
        "relative_humidity": 60.0    # %
    }
)
result_data = client.parse_result(result)
print(f"Temperature: 25°C, Humidity: 60%")
print(f"Dewpoint: {result_data}°C\n")

## Step 6: Check for heatwave conditions
print("Step 6: Heatwave Detection")
temperatures = [32, 34, 35, 36, 35, 34]  # °C over 6 days
result = await client.session.call_tool(
    "atm_check_heatwave",
    arguments={
        "temperatures": temperatures,
        "threshold": 32.0,           # °C
        "min_duration": 3            # days
    }
)
result_data = client.parse_result(result)
print(f"Temperatures: {temperatures}°C")
print(f"Heatwave detected: {result_data}\n")

await client.disconnect()

Tool Descriptions

AtmSci-Tool Server:

• atm_calc_coriolis_parameter: Calculate Coriolis parameter (f = 2Ω sin φ)

  • Args: latitude (float) - Latitude in degrees
  • Returns: Coriolis parameter in s⁻¹

• atm_calc_geostrophic_wind: Calculate geostrophic wind from pressure gradient

  • Args: pressure_gradient_x, pressure_gradient_y (Pa/m), latitude (deg), air_density (kg/m³)
  • Returns: Wind components (u, v) in m/s

• atm_calc_heat_index: Calculate heat index (apparent temperature)

  • Args: temperature_f (°F), relative_humidity (%)
  • Returns: Heat index in °F

• atm_calc_potential_temperature: Calculate potential temperature

  • Args: temperature_k (K), pressure_pa (Pa)
  • Returns: Potential temperature in K

• atm_calc_dewpoint: Calculate dewpoint temperature

  • Args: temperature_c (°C), relative_humidity (%)
  • Returns: Dewpoint temperature in °C

• atm_check_heatwave: Detect heatwave conditions

  • Args: temperatures (list), threshold (°C), min_duration (days)
  • Returns: Boolean indicating heatwave presence

Input/Output

Inputs:

• Temperatures in K, °C, or °F (as specified)
• Pressures in Pa or hPa
• Relative humidity in %
• Latitude in degrees
• Air density in kg/m³

Outputs:

• Coriolis parameter: s⁻¹
• Wind speeds: m/s
• Temperatures: K, °C, or °F
• Boolean flags for conditions

Use Cases

• Weather forecasting and analysis
• Climate model validation
• Heat stress assessment for public health
• Aviation meteorology
• Agricultural meteorology
• Renewable energy site assessment
• Atmospheric research

Physical Interpretations

Coriolis Parameter:

• Positive in Northern Hemisphere, negative in Southern
• Zero at equator, maximum at poles
• Critical for large-scale atmospheric circulation

Geostrophic Wind:

• Theoretical wind resulting from pressure gradient force and Coriolis effect
• Valid above atmospheric boundary layer
• Actual winds deviate due to friction and other forces

Heat Index:

• 80°F: Caution (fatigue possible)

• 90°F: Extreme caution (heat exhaustion possible)

• 103°F: Danger (heat stroke likely)

• 125°F: Extreme danger

Potential Temperature:

• Temperature air parcel would have if brought adiabatically to reference pressure (1000 hPa)
• Conserved for adiabatic processes
• Used to identify air masses and atmospheric stability

Dewpoint:

• Temperature at which air becomes saturated
• Higher dewpoint = more moisture
• Dewpoint > 65°F feels humid
• Dewpoint depression (T - Td) indicates saturation level

Additional Atmospheric Tools

• atm_calc_standard_atmosphere: Calculate standard atmosphere properties
• generate_synthetic_sounding: Create atmospheric sounding profiles
• workflow_storm_diagnosis: Analyze storm conditions
• workflow_wind_site_assessment: Assess wind energy potential
• geo_calc_distance: Calculate geographic distances
• stats_calc_anomaly: Calculate climate anomalies
• stats_calc_rolling_mean: Compute running averages
• stats_linear_trend: Determine climate trends