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@@ -58,3 +58,87 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+alpha0_usa.tif filter=lfs diff=lfs merge=lfs -text
+alpha_usa.tif filter=lfs diff=lfs merge=lfs -text
+b_usa.tif filter=lfs diff=lfs merge=lfs -text
+beta_usa.tif filter=lfs diff=lfs merge=lfs -text
+dem_usa.tif filter=lfs diff=lfs merge=lfs -text
+facc_usa.tif filter=lfs diff=lfs merge=lfs -text
+fdir_usa.tif filter=lfs diff=lfs merge=lfs -text
+im_usa.tif filter=lfs diff=lfs merge=lfs -text
+ksat_usa.tif filter=lfs diff=lfs merge=lfs -text
+leaki_usa.tif filter=lfs diff=lfs merge=lfs -text
+wm_usa.tif filter=lfs diff=lfs merge=lfs -text
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+wofs_0704/subset_wofs_ALL_41_20250704_0600_0925.nc filter=lfs diff=lfs merge=lfs -text
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+wofs_0704/subset_wofs_ALL_61_20250704_0600_1105.nc filter=lfs diff=lfs merge=lfs -text
+wofs_0704/subset_wofs_ALL_62_20250704_0600_1110.nc filter=lfs diff=lfs merge=lfs -text
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+wofs_0704/subset_wofs_ALL_64_20250704_0600_1120.nc filter=lfs diff=lfs merge=lfs -text
+wofs_0704/subset_wofs_ALL_65_20250704_0600_1125.nc filter=lfs diff=lfs merge=lfs -text
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+wofs_0704/subset_wofs_ALL_68_20250704_0600_1140.nc filter=lfs diff=lfs merge=lfs -text
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+wofs_0704/subset_wofs_ALL_71_20250704_0600_1155.nc filter=lfs diff=lfs merge=lfs -text
+wofs_0704/subset_wofs_ALL_72_20250704_0600_1200.nc filter=lfs diff=lfs merge=lfs -text

data.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41a484e8e79327fd1660638dbd7394d0b8e64022ea183ae14eed5a9a9d60df8a
+size 159089592376

fix.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""
+In-place repair for a Zarr store with mismatched time dimension lengths.
+
+- Compares the length of the 1D `time` array with the leading dimension of
+  the 3D `maxunitstreamflow` array.
+- Resizes the longer one down to the shorter to restore consistency.
+
+Usage:
+  ml conda; conda activate credit
+  python /glade/work/li1995/FLASH/data/fix.py
+"""
+
+from __future__ import annotations
+
+import zarr
+
+
+STORE_PATH = "/glade/derecho/scratch/li1995/data/maxunitstreamflow_2021_2025_10min.zarr"
+
+
+def main() -> None:
+    root = zarr.open(STORE_PATH, mode="r+")
+
+    time_arr = root["time"]  # shape: (T,)
+    data_arr = root["maxunitstreamflow"]  # shape: (T, Y, X)
+    lat_arr = root.get("latitude")
+    lon_arr = root.get("longitude")
+
+    t_time = int(time_arr.shape[0])
+    t_data = int(data_arr.shape[0])
+    y_size = int(data_arr.shape[1])
+    x_size = int(data_arr.shape[2])
+
+    print(f"Before: time={t_time}, data_time={t_data}, data_shape={data_arr.shape}")
+    if lat_arr is not None:
+        print(f"Latitude size: {lat_arr.shape}")
+    if lon_arr is not None:
+        print(f"Longitude size: {lon_arr.shape}")
+
+    target_len = min(t_time, t_data)
+    if t_data > target_len:
+        print(f"Resizing maxunitstreamflow from {t_data} to {target_len}")
+        data_arr.resize((target_len, y_size, x_size))
+    if t_time > target_len:
+        print(f"Resizing time from {t_time} to {target_len}")
+        time_arr.resize((target_len,))
+
+    # Re-read and report
+    t_time2 = int(time_arr.shape[0])
+    t_data2 = int(data_arr.shape[0])
+    print(f"After: time={t_time2}, data_time={t_data2}, data_shape={data_arr.shape}")
+
+
+if __name__ == "__main__":
+    main()

maxunitstreamflow_to_zarr.py

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+#!/usr/bin/env python3
+
+"""
+Convert MRMS FLASH_CREST_MAXUNITSTREAMFLOW_00.00 GRIB2 tiles to a single Zarr store.
+
+- Reads 10-minute GRIB2 files by day from the input root (e.g.,
+  /glade/derecho/scratch/li1995/data/FLASH_CREST_MAXUNITSTREAMFLOW_00.00/YYYYMMDD/...).
+- Aligns latitude/longitude to an existing precip Zarr template that shares the grid.
+- Writes float32 data with Zarr chunking (time=1, lat=438, lon=875) using parallel I/O per day.
+- Appends day-by-day to keep memory bounded; can utilize larger memory (e.g., 128GB) by
+  increasing per-day parallelism.
+
+Testing (login node, small sample):
+  ml conda; conda activate credit
+  python /glade/work/li1995/FLASH/data/maxunitstreamflow_to_zarr.py \
+    --input-root /glade/derecho/scratch/li1995/data/FLASH_CREST_MAXUNITSTREAMFLOW_00.00 \
+    --template-zarr /glade/derecho/scratch/li1995/data/precip_2021_2025.zarr \
+    --output-zarr /glade/derecho/scratch/li1995/data/maxunitstreamflow_2021_2025_10min.zarr \
+    --start 2021-01-01 --end 2021-01-02 \
+    --workers 8 --test-limit 36
+
+Full run (batch node with more memory):
+  python /glade/work/li1995/FLASH/data/maxunitstreamflow_to_zarr.py \
+    --input-root /glade/derecho/scratch/li1995/data/FLASH_CREST_MAXUNITSTREAMFLOW_00.00 \
+    --template-zarr /glade/derecho/scratch/li1995/data/precip_2021_2025.zarr \
+    --output-zarr /glade/derecho/scratch/li1995/data/maxunitstreamflow_2021_2025_10min.zarr \
+    --start 2021-01-01 --end 2025-08-01 \
+    --workers 24
+"""
+
+from __future__ import annotations
+
+import argparse
+import concurrent.futures
+import datetime as dt
+import os
+import re
+from pathlib import Path
+from typing import Iterable, List, Optional, Sequence, Tuple
+
+import numpy as np
+import xarray as xr
+from zarr.codecs import Zstd as ZstdCodec
+
+# Avoid overly verbose warnings when opening many GRIBs
+os.environ.setdefault("CFGRIB_LOGGING_LEVEL", "ERROR")
+
+
+FILENAME_RE = re.compile(r"_([0-9]{8})-([0-9]{6})\.grib2$")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Build MAXUNITSTREAMFLOW Zarr from daily MRMS GRIB2 files"
+    )
+    parser.add_argument(
+        "--input-root",
+        type=str,
+        required=True,
+        help="Root directory containing YYYYMMDD/ with GRIB2 files",
+    )
+    parser.add_argument(
+        "--template-zarr",
+        type=str,
+        required=True,
+        help="Existing precip Zarr with matching latitude/longitude",
+    )
+    parser.add_argument(
+        "--output-zarr",
+        type=str,
+        required=True,
+        help="Output Zarr store path to create/append",
+    )
+    parser.add_argument(
+        "--start",
+        type=str,
+        default="2021-01-01",
+        help="Start date (YYYY-MM-DD)",
+    )
+    parser.add_argument(
+        "--end",
+        type=str,
+        default="2025-08-01",
+        help="End date inclusive (YYYY-MM-DD)",
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=16,
+        help="Parallel workers for per-day GRIB reads",
+    )
+    parser.add_argument(
+        "--test-limit",
+        type=int,
+        default=0,
+        help="If >0, limit number of timesteps per day for testing",
+    )
+    parser.add_argument(
+        "--rechunk-lat",
+        type=int,
+        default=438,
+        help="Latitude chunk size",
+    )
+    parser.add_argument(
+        "--rechunk-lon",
+        type=int,
+        default=875,
+        help="Longitude chunk size",
+    )
+    return parser.parse_args()
+
+
+def daterange(start: dt.date, end_inclusive: dt.date) -> Iterable[dt.date]:
+    day = start
+    one = dt.timedelta(days=1)
+    while day <= end_inclusive:
+        yield day
+        day += one
+
+
+def list_grib2_files(day_dir: Path) -> List[Path]:
+    if not day_dir.is_dir():
+        return []
+    return sorted(p for p in day_dir.iterdir() if p.name.endswith(".grib2"))
+
+
+def extract_datetime_from_name(path: Path) -> Optional[np.datetime64]:
+    m = FILENAME_RE.search(path.name)
+    if not m:
+        return None
+    ymd, hms = m.group(1), m.group(2)
+    try:
+        dt_obj = dt.datetime.strptime(f"{ymd}{hms}", "%Y%m%d%H%M%S")
+        return np.datetime64(dt_obj)
+    except Exception:
+        return None
+
+
+def open_grib2_as_array(
+    file_path: Path,
+    target_shape: Tuple[int, int],
+    missing_to_nan: bool = True,
+) -> np.ndarray:
+    """Open GRIB2 with cfgrib and return data as float32 array (1, y, x).
+
+    Chooses the first data variable that has both latitude and longitude in dims.
+    """
+    ds = xr.open_dataset(
+        file_path.as_posix(), engine="cfgrib", backend_kwargs={}, chunks={}
+    )
+    try:
+        var_name = None
+        for name, da in ds.data_vars.items():
+            if "latitude" in da.dims and "longitude" in da.dims:
+                var_name = name
+                break
+        if var_name is None:
+            # fall back to first variable
+            var_name = next(iter(ds.data_vars.keys()))
+        da = ds[var_name]
+        data = da.values  # loads into memory for this tile
+        if data.ndim == 2:
+            data2d = data
+        elif data.ndim == 3 and data.shape[0] == 1:
+            data2d = data[0]
+        else:
+            raise ValueError(
+                f"Unexpected data shape {data.shape} in {file_path.name} for var {var_name}"
+            )
+        if data2d.shape != target_shape:
+            raise ValueError(
+                f"Array shape {data2d.shape} != expected {target_shape} for {file_path.name}"
+            )
+        arr = data2d.astype(np.float32, copy=False)
+        if missing_to_nan:
+            # Replace typical GRIB missing sentinel with NaN if present
+            # 3.4028235e+38 is float32 max
+            arr = np.where(arr >= np.finfo(np.float32).max * 0.99, np.nan, arr)
+        # add time axis
+        return arr[None, ...]
+    finally:
+        ds.close()
+
+
+def datetime64_to_epoch_seconds(times: Sequence[np.datetime64]) -> np.ndarray:
+    """Convert datetime64 values to float64 seconds since 1970-01-01.
+
+    This avoids cftime issues and matches the precip Zarr convention.
+    """
+    if len(times) == 0:
+        return np.array([], dtype=np.float64)
+    t_ns = np.array(times, dtype="datetime64[ns]")
+    base = np.datetime64("1970-01-01", "ns")
+    delta_ns = (t_ns - base).astype("timedelta64[ns]").astype(np.int64)
+    return (delta_ns.astype(np.float64) / 1e9).astype(np.float64)
+
+
+def build_day_arrays(
+    grib_files: Sequence[Path],
+    lat_size: int,
+    lon_size: int,
+    workers: int,
+    test_limit: int,
+) -> Tuple[np.ndarray, List[np.datetime64]]:
+    """Read a day's GRIB tiles in parallel → stacked (T, Y, X) float32 and time list."""
+    if test_limit > 0:
+        grib_files = grib_files[:test_limit]
+
+    times: List[Tuple[np.datetime64, int]] = []
+    for i, f in enumerate(grib_files):
+        t = extract_datetime_from_name(f)
+        if t is not None:
+            times.append((t, i))
+    # Keep only files with parseable time
+    if not times:
+        return np.empty((0, lat_size, lon_size), dtype=np.float32), []
+    times_sorted = sorted(times, key=lambda x: x[0])
+    sorted_indices = [i for _, i in times_sorted]
+    sorted_files = [grib_files[i] for i in sorted_indices]
+    sorted_times = [t for t, _ in times_sorted]
+
+    target_shape = (lat_size, lon_size)
+
+    def _load(path: Path) -> np.ndarray:
+        return open_grib2_as_array(path, target_shape=target_shape, missing_to_nan=True)
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as ex:
+        arrays = list(ex.map(_load, sorted_files))
+
+    if not arrays:
+        return np.empty((0, lat_size, lon_size), dtype=np.float32), []
+
+    stacked = np.concatenate(arrays, axis=0)
+    return stacked, sorted_times
+
+
+def ensure_output_store(
+    output_zarr: str,
+    var_name: str,
+    lat: xr.DataArray,
+    lon: xr.DataArray,
+    chunks: Tuple[int, int, int],
+    compressor: ZstdCodec,
+) -> None:
+    """Create the Zarr store if missing with metadata and encodings."""
+    if Path(output_zarr).exists():
+        return
+    empty = xr.DataArray(
+        data=np.empty((0, lat.size, lon.size), dtype=np.float32),
+        dims=("time", "latitude", "longitude"),
+        coords={
+            # Use float64 seconds since epoch to mirror precip store
+            "time": np.array([], dtype=np.float64),
+            "latitude": lat,
+            "longitude": lon,
+        },
+        name=var_name,
+        attrs={"long_name": "MAXUNITSTREAMFLOW", "units": "unknown"},
+    ).to_dataset(name=var_name)
+    # Add CF-like attributes for time
+    empty["time"].attrs = {
+        "standard_name": "time",
+        "long_name": "time",
+        "units": "seconds since 1970-01-01",
+        "calendar": "proleptic_gregorian",
+    }
+    encoding = {
+        var_name: {"dtype": "float32", "chunks": chunks, "compressors": (compressor,)},
+        # store as float64 seconds
+        "time": {"dtype": "float64"},
+    }
+    empty.to_zarr(
+        output_zarr,
+        mode="w",
+        consolidated=True,
+        encoding=encoding,
+        zarr_format=3,
+    )
+
+
+def append_day(
+    output_zarr: str,
+    var_name: str,
+    day_data: np.ndarray,
+    day_times: Sequence[np.datetime64],
+    lat: xr.DataArray,
+    lon: xr.DataArray,
+    chunks: Tuple[int, int, int],
+    compressor: ZstdCodec,
+) -> None:
+    if day_data.size == 0:
+        return
+    # Convert filename datetimes to epoch seconds (float64)
+    time_seconds = datetime64_to_epoch_seconds(day_times)
+
+    ds = xr.Dataset(
+        {
+            var_name: xr.DataArray(
+                day_data,
+                dims=("time", "latitude", "longitude"),
+                coords={"time": time_seconds, "latitude": lat, "longitude": lon},
+                attrs={"long_name": "MAXUNITSTREAMFLOW", "units": "unknown"},
+            )
+        }
+    )
+    # Ensure time coordinate attributes persist on append
+    ds["time"].attrs = {
+        "standard_name": "time",
+        "long_name": "time",
+        "units": "seconds since 1970-01-01",
+        "calendar": "proleptic_gregorian",
+    }
+    # Ensure no per-variable encodings are carried into append (xarray will error if present)
+    if var_name in ds:
+        ds[var_name].encoding = {}
+    if "time" in ds:
+        ds["time"].encoding = {}
+    if "latitude" in ds:
+        ds["latitude"].encoding = {}
+    if "longitude" in ds:
+        ds["longitude"].encoding = {}
+    ds.to_zarr(
+        output_zarr,
+        mode="a",
+        append_dim="time",
+        consolidated=False,
+        zarr_format=3,
+    )
+
+
+def get_existing_max_time(output_zarr: str) -> Optional[np.datetime64]:
+    p = Path(output_zarr)
+    if not p.exists():
+        return None
+    try:
+        ds_out = xr.open_zarr(output_zarr, consolidated=True, decode_times=False)
+    except Exception:
+        ds_out = xr.open_zarr(output_zarr, consolidated=False, decode_times=False)
+    try:
+        if "time" in ds_out.coords and ds_out.sizes.get("time", 0) > 0:
+            tvar = ds_out["time"]
+            tvals = tvar.values
+            last = tvals[-1]
+            # Cases: datetime64, numeric epoch, or cftime/object
+            if isinstance(last, np.datetime64):
+                return last.astype("datetime64[ns]")
+            if np.issubdtype(tvals.dtype, np.datetime64):
+                return np.array(last, dtype="datetime64[ns]")
+            if np.issubdtype(tvals.dtype, np.number):
+                units = (tvar.attrs or {}).get("units", "seconds since 1970-01-01")
+                base_match = re.match(r"(seconds|milliseconds|microseconds|nanoseconds|minutes|hours|days) since (\d{4}-\d{2}-\d{2})", units)
+                if base_match:
+                    unit, base_date = base_match.groups()
+                    base = np.datetime64(base_date, "s")
+                    # Map unit to numpy timedelta unit
+                    unit_map = {
+                        "nanoseconds": "ns",
+                        "microseconds": "us",
+                        "milliseconds": "ms",
+                        "seconds": "s",
+                        "minutes": "m",
+                        "hours": "h",
+                        "days": "D",
+                    }
+                    np_unit = unit_map.get(unit, "s")
+                    delta = np.array(last, dtype=f"timedelta64[{np_unit}]")
+                    return (base + delta).astype("datetime64[ns]")
+                # Fallback treat as seconds since epoch
+                base = np.datetime64("1970-01-01", "s")
+                delta = np.array(last, dtype="timedelta64[s]")
+                return (base + delta).astype("datetime64[ns]")
+            # Attempt string conversion (e.g., cftime)
+            try:
+                return np.datetime64(str(last)).astype("datetime64[ns]")
+            except Exception:
+                return None
+        return None
+    finally:
+        ds_out.close()
+
+
+def main() -> None:
+    args = parse_args()
+
+    input_root = Path(args.input_root)
+    template_zarr = args.template_zarr
+    output_zarr = args.output_zarr
+
+    start_date = dt.datetime.strptime(args.start, "%Y-%m-%d").date()
+    end_date = dt.datetime.strptime(args.end, "%Y-%m-%d").date()
+
+    # Load template lat/lon from precip Zarr
+    ds_tpl = xr.open_zarr(template_zarr, consolidated=True)
+    try:
+        lat = ds_tpl["latitude"].load()
+        lon = ds_tpl["longitude"].load()
+    finally:
+        ds_tpl.close()
+
+    lat_size, lon_size = int(lat.size), int(lon.size)
+
+    var_name = "maxunitstreamflow"
+    chunks = (1, int(args.rechunk_lat), int(args.rechunk_lon))
+    compressor = ZstdCodec(level=3)
+
+    # Ensure store exists with metadata
+    ensure_output_store(output_zarr, var_name, lat, lon, chunks, compressor)
+
+    existing_max_time = get_existing_max_time(output_zarr)
+    if existing_max_time is not None:
+        print(f"Resuming after {existing_max_time}")
+
+    for day in daterange(start_date, end_date):
+        day_dir = input_root / day.strftime("%Y%m%d")
+        gribs = list_grib2_files(day_dir)
+        if not gribs:
+            continue
+        # Filter by resume point
+        if existing_max_time is not None:
+            gribs = [
+                f
+                for f in gribs
+                if (t := extract_datetime_from_name(f)) is not None and t > existing_max_time
+            ]
+            if not gribs:
+                continue
+        print(f"Processing {day_dir} with {len(gribs)} files ...")
+        day_data, day_times = build_day_arrays(
+            gribs,
+            lat_size=lat_size,
+            lon_size=lon_size,
+            workers=int(args.workers),
+            test_limit=int(args.test_limit),
+        )
+        if day_data.size == 0:
+            continue
+        append_day(
+            output_zarr,
+            var_name,
+            day_data,
+            day_times,
+            lat,
+            lon,
+            chunks,
+            compressor,
+        )
+
+    # Consolidate metadata at the end for faster reads
+    try:
+        import zarr as zarr_pkg
+
+        zarr_pkg.consolidate_metadata(output_zarr)
+    except Exception as e:
+        print(f"Consolidate metadata skipped: {e}")
+
+
+if __name__ == "__main__":
+    main()

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