How could I significantly boost the model variance, performance and accuracy while preserving low computational time and high resolution of the model? Here's the current cross-performance statistics of the model while runt during daytime. The temperature error rate is signficiantly decreased during nighttime, but the R2 and other variance parameter performance has been poor as you can see. Here's the statistics of the model runt at 5PM:

MAE: 1.296 ± 0.238

MSE: 2.746 ± 0.936

RMSE: 1.634 ± 0.279

R2: -0.012 ± 0.102

Median_Err: 1.052 ± 0.224

Exp_Variance: 0.082 ± 0.058

I'll help analyze the performance issues and suggest improvements while keeping computational efficiency in mind. Let me break this down systematically.

Temperature Analysis Model Improvements

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I've created a comprehensive improvement plan focusing on six key areas while maintaining computational efficiency. Let me highlight the most important points and explain why they should help:

Feature Engineering Improvements:

Added solar angle calculations for better daytime predictions

Introduced urban heat island indicators

Enhanced water body effects modeling

Added terrain complexity metrics These should significantly improve the R² score by better capturing physical relationships.

Model Architecture:

Implemented a stacking ensemble with XGBoost, LightGBM, and CatBoost

All models configured for GPU acceleration

Added residual correction mechanism This diversity should reduce variance while maintaining speed through GPU utilization.

Spatial Cross-Validation:

Implemented DBSCAN clustering for better spatial grouping

Enhanced group k-fold validation This should give you more reliable performance estimates and reduce overfitting.

Dynamic Learning Rate:

Added cosine annealing scheduler

Implemented dynamic callbacks This should help with both convergence and generalization.

Residual Analysis:

Added a separate residual correction model

Implemented weighted ensemble predictions This should help address systematic biases in predictions.

Computational Optimization:

Used float32 instead of float64

Implemented chunking for large datasets

GPU acceleration throughout These optimizations should maintain or improve speed while enabling more complex models.

The poor R² score (-0.012) suggests the current model isn't capturing the underlying relationships well. The new feature engineering should particularly help with daytime predictions by better modeling solar radiation and urban heat effects.

Would you like me to elaborate on any of these improvements or help implement a specific aspect first?

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Retry

JG

And how could I describe this project in simple words to search for similar projects on Google with a short search prompt?

Let me help create effective search prompts to find similar projects.

Search Strategies for High-Resolution Temperature Modeling Projects

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In simplest terms, your project could be described as:

"A high-resolution temperature mapping project that combines weather station data, environmental factors, and machine learning to create detailed temperature maps at 250-meter resolution"

The most effective short search prompts would be:

For code examples:

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"high resolution temperature mapping machine learning github"

For research papers:

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"mesoscale temperature prediction machine learning paper"

For methodology:

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"spatial temperature modeling environmental factors python"

These search terms should help you find similar projects while capturing the key aspects of your work: the high resolution, the machine learning approach, and the environmental factor integration. I've included a more comprehensive list of search strategies in the artifact above, which you can use to find more specific aspects of similar projects.

Would you like me to suggest some specific repositories or papers that might be particularly relevant to your project?

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Retry

JG

Any ideas on how I could effectively fix this model limitation on generating unnatural, linear transitions when the model is observing significant temperature contrast? When it see that, it just generates linear temperature transition in that area. For example, -2C on right and 2C on left, it will generate a straight linear transition between these contrasts. But I want the model to determine a more realistic transition without smoothing the data significantly and keeping it high resolution?

Let me help you address this linear transition issue. This is a common challenge in temperature modeling where the model falls back to simple linear interpolation when faced with sharp contrasts.

Enhanced Temperature Transition Modeling

Click to open code

I've created a comprehensive solution to address the linear transition issue. Here's how it works and why it should help:

Physical Constraint Integration:

The code introduces physical constraints based on:

Terrain roughness

Land cover boundaries

Wind patterns

Solar radiation

These constraints help create more natural temperature gradients

Micro-Feature Generation:

Generates microscale features for each point including:

Slope and aspect effects

Local wind patterns

Solar radiation exposure

This helps capture small-scale variations that affect temperature transitions

Adaptive Transition Modeling:

Uses identify_transition_zones() to detect areas with significant temperature contrasts

Applies different modeling approaches in transition zones

Maintains sharp contrasts where physically appropriate while smoothing unrealistic transitions

Physics-Based Corrections:

Implements standard atmospheric lapse rates

Accounts for terrain-following temperature gradients

Incorporates land cover influence on temperature patterns

Smart Smoothing:

The adaptive_smoothing() function only applies smoothing in identified transition zones

Uses a blending factor to maintain both sharpness and realism

Preserves important temperature gradients while removing artificial linearity

Key improvements this brings:

More Natural Transitions:

Temperature changes follow physical principles rather than linear interpolation

Transitions respect terrain and environmental features

Maintained Resolution:

The approach only modifies transition zones

Preserves sharp contrasts where they make physical sense

Keeps the 250m resolution intact

Physical Consistency:

All transitions follow basic meteorological principles

Incorporates terrain effects on temperature gradients

Accounts for land cover influences

To implement this, you would:

First identify your transition zones:

python

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enhancer = EnhancedTemperatureTransition()

transition_zones = enhancer.identify_transition_zones(points, temperatures)

Generate physical constraints:

python

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constraints = enhancer.generate_physical_constraints(points, terrain, land_cover)

Apply the enhanced transitions:

python

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enhanced_temperatures = enhancer.enhance_transitions(

    points, temperatures, terrain, land_cover, weather_data

)

Would you like me to:

Add any specific physical constraints that are particularly relevant to your region?

Adjust the parameters for your specific resolution and scale?

Explain any particular part of the implementation in more detail?

Copy

Retry

Claude can make mistakes.

Please double-check responses.

Any ideas on how I could effectively fix this model limitation on generating unnatural, linear transitions when the model is observing significant temperature contrast? When it see that, it just generates linear temperature transition in that area. For example, -2C on right and 2C on left, it will generate a straight linear transition between these contrasts. But I want the model to determine a more realistic transition without smoothing the data significantly and keeping it high resolution? Note that's I can't add any more data to the model than existing dataset in the model. Can't integrate wind data from weather stations, etc. Adjust the approach accordingly, and instead of writing code for the proposed solution now, please write the proposed solution only in long text format as if it would be a research paper. But not be too specific about code structure overall. And use code błocks for the text please

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The code implements a high-resolution mesoscale temperature analysis for the Masovian Voivodeship in Poland by integrating multiple data sources, applying advanced machine learning models, and incorporating environmental adjustments. The primary objective is to generate a detailed temperature map at a 250-meter resolution, capturing spatial temperature variations influenced by factors such as topography, land cover, water bodies, and urbanization. 

 

The script begins by importing necessary libraries for geospatial analysis, data manipulation, machine learning, and visualization. It sets up the environment by configuring the PROJ_LIB variable for coordinate reference systems and suppresses warnings to maintain clean output. Several functions are defined for data cleaning, fetching, and preprocessing to ensure that the data is standardized and accurate for analysis. 

 

Data is collected from ground-based stations and remote sensing products. Ground-based temperature data are sourced from Traxelektronik stations, Netatmo personal weather stations, and IMGW stations. Traxelektronik data are scraped from their website, Netatmo data are fetched using their API within the geographical bounds of the Masovian Voivodeship, and IMGW data are obtained from their API for specific target stations. 

 

Station names are cleaned using the clean_station_name function, which removes specified keywords and phrases to standardize them and improve geocoding accuracy. Geocoding of station locations is performed using the get_coordinates function with the Nominatim geocoder, ensuring that only stations within the Masovian boundary are considered valid. The is_in_masovian function checks whether a given point lies within the Masovian boundary by projecting points to a suitable coordinate system and performing spatial operations. 

 

Temperature readings are cleaned using the clean_temperature function, which removes non-numeric characters and converts strings to floats. Nearby duplicate stations are removed using the remove_nearby_duplicates function, which builds a KDTree for efficient neighbor searches and considers stations within a specified distance threshold as duplicates. Outliers are detected and removed using the Interquartile Range (IQR) method to ensure that extreme values do not skew the analysis. 

 

Feature engineering is performed to create a comprehensive dataset for modeling. Spatial features such as elevation, slope, aspect, tree cover density, water body proximity, and distance to water bodies are extracted. Elevation values are obtained from a Digital Elevation Model (DEM) using the get_elevation_at_points function. Slope and aspect are calculated using the compute_slope_aspect function, and northness and eastness are derived for modeling purposes. Tree cover percentages are retrieved from the forest cover raster, and water body proximity is determined using the get_water_body_at_points function. A distance transform computes the shortest distance from each point to the nearest water body. 

 

Anthropogenic features include population density and built-up areas. Population density values are extracted from the LandScan HD dataset, and built-up area values are retrieved from the human settlement dataset. Land cover classification is performed using the CORINE Land Cover dataset, mapping detailed land cover classes to broader categories such as urban, forest, and water. One-hot encoding is applied to these categories to create dummy variables for modeling. 

 

Temporal features are incorporated to capture cyclical patterns associated with the time of day. The current hour in Central European Time is obtained, and a boolean flag indicates whether it is daytime based on predefined thresholds. Sinusoidal encoding is applied to the hour to model cyclical patterns, and interaction terms between elevation and temporal features are created to model interactions between features. 

 

Machine learning modeling involves data preparation, spatial cross-validation, model definition, hyperparameter tuning, and model evaluation. Relevant features are selected for modeling, and any remaining missing values are handled appropriately. Sample weights are assigned to samples based on their data source to reflect data quality. Spatial cross-validation is performed using GroupKFold to ensure that folds are spatially independent, preventing data leakage. 

 

The model comprises base estimators including Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor configured to use GPU acceleration for efficiency. A Ridge Regression model serves as the meta-estimator in the stacking ensemble. Hyperparameter tuning is conducted using RandomizedSearchCV, which searches over specified hyperparameter distributions to find the best model configuration. The model is evaluated using metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, R² Score, Median Absolute Error, and Explained Variance. Permutation importance is used to assess the importance of each feature by measuring the decrease in model performance when the feature's values are shuffled. 

 

Spatial interpolation and environmental adjustments are applied to enhance the temperature predictions. A regular grid with 250-meter resolution is generated over the Masovian Voivodeship, and the same features used for stations are computed for each grid point. The trained stacking model predicts temperature values at each grid point. Ordinary Kriging is used to interpolate the residuals (differences between observed and predicted temperatures) over the grid. The interpolated residuals are added back to the predicted temperatures to refine the estimates. 

 

Environmental adjustments include topographic cooling, forest cooling effect, water body temperature adjustment, and ECOSTRESS anomaly integration. Topographic cooling adjusts temperatures based on elevation, particularly during nighttime when cooler temperatures are expected at higher altitudes. The forest cooling effect applies a cooling adjustment based on tree cover density, time of day, and proximity to water bodies. The compute_dynamic_forest_cooling function models this effect, considering that cooling is stronger during the day and in areas with higher tree cover, with proximity to water enhancing the cooling due to increased humidity. 

 

The water body temperature adjustment models the temperature over water bodies using a physics-based approach that considers heat flux components such as solar radiation, outgoing longwave radiation, evaporation, and convection. The compute_water_body_temperature_adjustment function calculates the net heat flux and the corresponding temperature change, which is smoothed and applied to grid points over water. ECOSTRESS anomalies are integrated by computing the difference between the ECOSTRESS Land Surface Temperature (LST) and its mean, normalizing the anomalies, and applying them to the temperature estimates, weighted based on the time of day. 

 

Visualization is performed using Matplotlib to create temperature maps. A custom color scale is defined based on a CSV file specifying color mappings for temperature values. The Masovian boundary and station locations are overlaid on the map. Temperature contours are added to highlight gradients, and minimum and maximum temperature locations are identified, reverse geocoded to obtain place names, and labeled on the map. High-resolution plots are created for specific areas to showcase the model's ability to capture fine-scale variability, with aesthetic enhancements made for visual appeal. 

 

Finally, the trained stacking model is saved using joblib for future use, and the code outputs interpolated temperature values at specified test coordinates. The overall result is a high-resolution temperature map of the Masovian Voivodeship, capturing spatial variations influenced by topography, land cover, water bodies, and urbanization. This approach demonstrates the effectiveness of combining diverse data sources and advanced modeling techniques to produce detailed temperature maps suitable for various applications in meteorology, agriculture, urban planning, and environmental monitoring. — KEY TASK: Any ideas on how I could effectively fix this model limitation on generating unnatural, linear transitions when the model is observing significant temperature contrast? When it see that, it just generates linear temperature transition in that area. For example, -2C on right and 2C on left, it will generate a straight linear transition between these contrasts. But I want the model to determine a more realistic transition without smoothing the data significantly and keeping it high resolution? Note that's I can't add any more data to the model than existing dataset in the model. Can't integrate wind data from weather stations, etc. Adjust the approach accordingly, and instead of writing code for the proposed solution now, please write the proposed solution only in long text format as if it would be a research paper. But not be too specific about code structure overall. And use code błocks for the text please