CESM3 Data Errors: Troubleshooting Daily/Sub-Daily Issues

Hey guys! Let's dive into a common issue we've been seeing with CESM3 daily or sub-daily data that results in errors when using GenTS. This article will break down the problem, the error messages, and potential solutions. We'll keep it casual and focus on getting you the information you need to troubleshoot effectively. So, if you're struggling with this, you're in the right place! We will explore a detailed discussion of the errors encountered while processing daily and sub-daily data with CESM3, specifically within the GenTS framework. This includes a comprehensive analysis of error messages, problematic data streams, and potential solutions.

Understanding the Issue with CESM3 and GenTS

When working with climate models like CESM3, you often need to process large amounts of data, including daily and sub-daily outputs. GenTS is a tool designed to help with this, but sometimes, things don't go as planned. Specifically, users have encountered issues when trying to process daily or sub-daily data, leading to errors and frustration. The core issue revolves around how GenTS handles certain types of data files, particularly those with high temporal resolution. So, let's break down the specifics.

Key Challenges with Daily and Sub-Daily Data

1. Metadata Inconsistencies: One of the primary hurdles is that GenTS struggles with inconsistencies in metadata within the data files. This is particularly noticeable in files like the atm history files, such as h2i (daily instantaneous), h3a (3-hourly averages), h3i (3-hourly instantaneous), and h4i (monthly instantaneous). The error messages often point to GenTS being unable to pull valid or complete metadata for these files. For example, the error message "Could not pull valid/complete metadata for '/glade/derecho/scratch/asphilli/archive/GenTS_testdata/CESM3_GenTS_totest/atm/hist/b.e30_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.226.cam.h2i.0001-01-02-00000.nc'" indicates that GenTS can't read the file's metadata properly, which is crucial for processing.

2. Time Bounds and Sorting: Another issue arises with the time bounds variable. GenTS often defaults to using the time variable if it can't find an equivalent time bounds variable. This can lead to problems in sorting the data along the time dimension. The error message "Unable to find equivalent 'time bounds' variable. Defaulting to 'time'" is a common indicator of this issue. Furthermore, the traceback error, ending with ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all(), shows a deeper problem in the time-sorting logic within GenTS. This error typically occurs in the sort_along_time function of the HFCollection class, suggesting that the time arrays being compared have ambiguous truth values, likely due to inconsistencies or missing data.

3. File-Specific Problems: Some file types within CESM3's output are inherently problematic for GenTS. For instance, the ocn (ocean) data streams, specifically mom6.h.*.YYYY-MM.nc.???? (monthly channel/strait data) and h.sfc files (daily data), have been previously reported to cause issues. These files may have unique structures or metadata that GenTS isn't designed to handle out of the box.

4. Missing Variables: The atm h2i and h3i files sometimes lack frequently used multi-dimensional CAM variables. While this might not seem like a critical issue, it can throw off GenTS, which expects certain variables to be present for proper processing. However, even with this, the variables like co2vmr, ch4vmr, n2ovmr, f11vmr, f12vmr, and sol_tsi should still be concatenated, but the absence of other expected variables can disrupt the workflow.

Decoding the Error Messages

Let's break down some of those error messages to understand what they're telling us. This will help you when you're staring at your screen wondering what went wrong.

Key Error Messages and What They Mean

1. "Unable to find equivalent 'time bounds' variable. Defaulting to 'time'."

   • What it means: GenTS couldn't find the time bounds variable in the file's metadata, which is used to define the start and end times for each time step. It's falling back to the basic time variable, which might not be precise enough for sub-daily data. This can lead to incorrect time sorting and processing.
   • Why it matters: In climate data, especially with high temporal resolution, accurately defining time boundaries is crucial. Without time bounds, GenTS might misinterpret the data's temporal structure.

2. "Could not pull valid/complete metadata for '[filepath]'."

   • What it means: GenTS is unable to read essential information from the file's header. This could be due to a corrupted file, an unsupported file format, or inconsistencies in the metadata structure.
   • Why it matters: Metadata includes crucial information like variable names, units, dimensions, and time information. Without it, GenTS can't understand the data within the file.

3. "ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"

   • What it means: This is a Python-specific error that arises when comparing arrays with more than one element without a clear aggregation method (any() or all()). In the context of GenTS, it usually means there's an issue when sorting data along the time dimension.
   • Why it matters: This error typically points to a fundamental problem in the time-handling logic. It suggests that the time arrays being compared have ambiguous values, possibly due to missing or inconsistent time data.

Script Syntax and How It Leads to Errors

Let's take a look at the Python script used to process the CESM3 data and see how the syntax might be contributing to these errors.

Analyzing the Script

from gents.hfcollection import HFCollection
from gents.timeseries import TSCollection

input_head_dir = "/glade/derecho/scratch/asphilli/archive/GenTS_testdata/CESM3_GenTS_totest/"    
output_head_dir = "/glade/derecho/scratch/asphilli/GenTS/CESM3_GenTS_totest/"
hf_collection = HFCollection(input_head_dir)
hf_collection.pull_metadata()
ts_collection = TSCollection(hf_collection, output_head_dir)
ts_collection.execute()

This script is pretty straightforward. It initializes HFCollection to gather metadata from the input directory, and then uses TSCollection to process the data. The error occurs during the initialization of TSCollection, specifically in the hf_collection.sort_along_time() function. Let's break it down:

1. HFCollection and pull_metadata(): The HFCollection class is used to manage a collection of history files. The pull_metadata() method reads metadata from these files, storing information about variables, dimensions, and time.
2. TSCollection Initialization: The TSCollection class processes time series data. During initialization, it calls hf_collection.sort_along_time() to sort the files by time. This is where the ValueError occurs.
3. The sort_along_time() Function: This function is the culprit. It sorts the history files based on time, using a lambda function to extract the time values. The traceback points directly to this function, indicating that the sorting process is failing due to ambiguous time values.

Potential Issues in the Script

• Metadata Retrieval: The pull_metadata() function might not be robust enough to handle the inconsistencies in CESM3's daily and sub-daily data files. If it fails to extract time information correctly, the sorting process will be flawed.
• Time Sorting Logic: The lambda function used for sorting might not be able to handle arrays with ambiguous truth values. This suggests that the time arrays being compared have more than one element, and the comparison is not resolving to a single Boolean value.

Steps to Troubleshoot and Resolve the Errors

Alright, let's get practical. Here’s a step-by-step guide to troubleshooting these errors. We'll go through checking your data, modifying your script, and even considering workarounds.

1. Verify the Data Files

First things first, let’s make sure your data files aren't corrupted and contain the necessary information.

• Check File Integrity: Use tools like ncdump or ncview to inspect the NetCDF files. Look for any signs of corruption or missing data.
• Inspect Metadata: Use ncdump -h to view the header information. Ensure that time variables and other essential metadata are present and consistent.
• Review Problematic Streams: Focus on the identified problematic streams (atm h2i, h3a, h3i, h4i; ocn mom6.h.*, h.sfc) and see if there's a common pattern in their metadata.

2. Modify the Script

Next, we can adjust the script to handle the specific issues we've identified.

• Improve Metadata Handling:
  • Modify the pull_metadata() function in HFCollection to be more robust. Add error handling to catch files with missing or inconsistent metadata.
  • Consider using a more flexible metadata parsing approach, such as directly accessing the NetCDF files using libraries like netCDF4 or xarray.
• Handle Time Sorting:
  • Adjust the sort_along_time() function to handle ambiguous time values. Use a.any() or a.all() when comparing time arrays.
  • Implement a more sophisticated time-sorting algorithm that can handle missing or irregular time steps.
• Selective File Processing:
  • Implement logic to skip problematic files or streams. For instance, you might exclude the ocn files known to cause issues.
  • Process different streams separately, applying custom handling for each.

Here’s an example of how you might modify the sort_along_time() function:

import numpy as np

def sort_along_time(self):
    try:
        sorted_map = dict(sorted(self.__hf_to_meta_map.items(), key=lambda item: item[1].get_float_times()))
    except ValueError as e:
        if "The truth value of an array with more than one element is ambiguous" in str(e):
            # Handle the ambiguous time values
            sorted_map = dict(sorted(self.__hf_to_meta_map.items(), key=lambda item:
                                     np.mean(item[1].get_float_times()) if len(item[1].get_float_times()) > 1 else item[1].get_float_times()))
        else:
            raise e
    self.__hf_to_meta_map = sorted_map

This modification attempts to handle the ValueError by taking the mean of the time values if there's more than one, providing a single value for sorting. It's a basic fix, but it can help in many cases.

3. Implement Workarounds

Sometimes, the best approach is to sidestep the issue altogether. Here are a couple of workarounds you can try:

• Pre-process the Data:
  • Use other tools (like Climate Data Operators, CDO) to pre-process the data, ensuring consistent metadata and handling time variables.
  • Split the data into smaller chunks or time ranges that GenTS can handle more easily.
• Alternative Tools:
  • Explore other time series processing tools that might be more robust for CESM3 data. Libraries like xarray are excellent for handling climate data and might offer better support for complex time structures.

4. Test and Iterate

After making changes, always test your script thoroughly. Run it on a subset of your data and check for errors. Iterate on your changes based on the results.

• Start Small: Process a small set of files to ensure your changes are working correctly.
• Monitor Error Messages: Keep an eye on the error messages and logs. They'll give you clues about what’s still going wrong.
• Document Your Changes: Keep track of the changes you make and why. This will help you (and others) understand the troubleshooting process.

Conclusion

Dealing with errors in data processing can be a pain, but by understanding the error messages, analyzing your script, and implementing targeted solutions, you can overcome these challenges. Remember, the key is to break down the problem into smaller parts and tackle them one by one. Whether it's improving metadata handling, adjusting time-sorting logic, or pre-processing your data, there's usually a way to get things working.

So, guys, don't get discouraged! Keep experimenting, keep learning, and you'll get there. If you have specific solutions or insights, please share them in the comments below—let's help each other out! And don't forget, tackling CESM3 daily or sub-daily data issues often requires a combination of careful analysis, strategic code adjustments, and a bit of patience. Happy processing!