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Stacking NASA POWER and PVGIS rasters in rasterio

Scenario / symptom: you call rasterio.merge or rasterio.stack on a NASA POWER daily Global Horizontal Irradiance (GHI) grid and a PVGIS Typical Meteorological Year (TMY) surface, and you get ValueError: Input shapes do not overlap raster — or worse, no exception at all, just a stacked array whose two bands disagree on pixel registration by half a cell and quietly poison every downstream capacity factor. This failure lands in the raster-stacking stage of multi-source resource assessment, the step where heterogeneous irradiance products are supposed to become a single analysis-ready grid. It is the concrete, two-source instance of the CRS drift in multi-source raster stacks failure mode dissected in the parent workflow, Solar Irradiance Raster Processing, itself a stage of the broader Solar & Wind Resource Modeling Workflows pipeline.

This page isolates the compounding causes, gives a pre-flight guard that surfaces the mismatch before a single byte is merged, then builds an explicit reproject-and-stack routine that produces a deterministic, audit-ready two-band GeoTIFF. The discipline is the same one applied across the site: enforce a known coordinate reference system and grid registration before any raster algebra, never let rasterio reproject implicitly, and gate the output against a structural audit.

Root-Cause Analysis

The failure is rarely a rasterio bug. It stems from three compounding spatial mismatches that violate the strict alignment requirements of raster algebra:

  1. Native CRS & Grid Registration: NASA POWER distributes data on a 0.5° × 0.5° latitude/longitude grid (EPSG:4326) with center-registered pixels. PVGIS outputs are typically projected to UTM zones or delivered as 0.01° grids with edge-registered (corner-aligned) pixels. Mixing registration types shifts pixel centers by half a cell width, introducing systematic irradiance bias.
  2. Affine Transform Divergence: rasterio.merge relies on GDAL’s VRT builder to compute a unified bounding box. When input transforms differ in origin, resolution, or rotation, the builder cannot resolve overlapping extents, triggering shape overlap errors or silent clipping.
  3. Implicit Reprojection Memory Spike: If rasterio attempts on-the-fly resampling during merge, it materializes full-resolution arrays in RAM before alignment. For continental-scale datasets, this routinely triggers MemoryError on standard analyst workstations (≤32 GB RAM).

Resolving this requires explicit pre-alignment, memory-aware I/O, and deterministic fallback routing.

From compounding mismatch to an audited two-band stack A top-down diagram. The top row holds three causes of the merge failure: registration mismatch between center- and edge-registered pixel grids, affine transform divergence in origin, resolution and rotation, and the implicit-reprojection RAM spike. All three drop into one wide pre-stack validation gate that hard-fails on disjoint bounds, resolution, CRS and registration. From the gate the flow continues left to right through a build-unified-target-grid stage, an explicit per-source reproject stage with bilinear for NASA POWER and average for PVGIS, and a highlighted terminal stage that stacks the bands into an LZW GeoTIFF with audit tags and a CI gate. ! Registration mismatch center- vs edge-registered half-cell pixel shift ! Affine divergence origin · resolution · rotation VRT cannot resolve extent ! Implicit reproject full-res arrays in RAM MemoryError at scale Pre-stack validation gate — hard fail, not warning disjoint_bounds · resolution · CRS · registration Build unified target grid from_origin · float32 NaN nodata Explicit reproject per source bilinear · NASA POWER average · PVGIS Stack → LZW GeoTIFF 2 bands · audit tags CI/CD integrity gate

Pre-Stack Spatial Validation Protocol

Before attempting any merge operation, enforce deterministic spatial validation. This prevents silent drift in the resource-processing pipeline and ensures audit-ready traceability — the same guard-before-operation pattern used when aligning EPSG:4326 and EPSG:3857 for solar site mapping. Run it as a hard gate, not a warning: an unvalidated stack that reaches a yield model is indistinguishable from a correct one until the project finance review fails.

python
import rasterio
from rasterio.coords import disjoint_bounds
from rasterio.transform import from_origin
import numpy as np

def validate_alignment(src_paths, target_crs="EPSG:4326", target_res=0.01):
    """Pre-flight validation for multi-source raster alignment."""
    transforms = []
    for path in src_paths:
        with rasterio.open(path) as src:
            # Check CRS compatibility
            if not src.crs.equals(target_crs):
                raise ValueError(f"{path} CRS {src.crs} != target {target_crs}. Reprojection required.")

            # Check bounds intersection
            if len(src_paths) > 1:
                for other_path in src_paths:
                    if path != other_path:
                        with rasterio.open(other_path) as other:
                            if disjoint_bounds(src.bounds, other.bounds):
                                raise ValueError(f"Disjoint bounds between {path} and {other_path}")

            transforms.append(src.transform)

    # Verify uniform resolution post-resampling
    res_a = [abs(t.a) for t in transforms]
    if not np.allclose(res_a, target_res, atol=1e-6):
        raise ValueError(f"Input resolutions {res_a} diverge from target {target_res}.")

    print("✅ Spatial validation passed. Proceeding to alignment.")

Memory-Aware Alignment & Stacking Routine

Bypassing rasterio.merge for explicit reproject + np.stack provides deterministic control over resampling kernels, nodata handling, and memory allocation. The routine below enforces float32 precision, LZW compression, and explicit transform logging for downstream audit compliance.

python
import rasterio
from rasterio.warp import reproject, Resampling
from rasterio.transform import from_origin
import numpy as np
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def align_and_stack_solar_rasters(power_path, pvgis_path, out_path,
                                  target_crs="EPSG:4326", target_res=0.01):
    """Aligns, resamples, and stacks NASA POWER and PVGIS irradiance rasters."""

    with rasterio.open(power_path) as src_power, rasterio.open(pvgis_path) as src_pvgis:
        # 1. Establish unified target grid
        target_bounds = rasterio.warp.transform_bounds(
            src_pvgis.crs, target_crs, *src_pvgis.bounds
        )
        target_transform = from_origin(
            target_bounds[0], target_bounds[3], target_res, target_res
        )
        width = int(np.ceil((target_bounds[2] - target_bounds[0]) / target_res))
        # target_bounds = (left, bottom, right, top); height = (top - bottom) / res
        height = int(np.ceil((target_bounds[3] - target_bounds[1]) / target_res))

        # 2. Pre-allocate destination arrays (float32 halves RAM vs float64)
        dst_shape = (1, height, width)
        dst_power = np.full(dst_shape, np.nan, dtype="float32")
        dst_pvgis = np.full(dst_shape, np.nan, dtype="float32")

        # 3. Explicit reprojection & resampling
        datasets = [
            (src_power, dst_power, Resampling.bilinear),
            (src_pvgis, dst_pvgis, Resampling.average)
        ]

        for src, dst_arr, resample_method in datasets:
            reproject(
                source=rasterio.band(src, 1),
                destination=dst_arr,
                src_transform=src.transform,
                src_crs=src.crs,
                dst_transform=target_transform,
                dst_crs=target_crs,
                resampling=resample_method,
                dst_nodata=np.nan
            )

        # 4. Stack & write with audit metadata
        stacked = np.concatenate([dst_power, dst_pvgis], axis=0)
        profile = src_power.profile.copy()
        profile.update({
            "driver": "GTiff",
            "dtype": "float32",
            "count": 2,
            "height": height,
            "width": width,
            "crs": target_crs,
            "transform": target_transform,
            "compress": "lzw",
            "nodata": np.nan,
            "tiled": True,
            "blockxsize": 512,
            "blockysize": 512
        })

        with rasterio.open(out_path, "w", **profile) as dst:
            dst.write(stacked)
            dst.update_tags(
                source_1="NASA_POWER_GHI",
                source_2="PVGIS_TMY_GHI",
                resampling="bilinear/average",
                target_crs=str(target_crs),
                target_resolution=str(target_res)
            )

    logger.info(f"Stacked raster written to {out_path} | Shape: {stacked.shape} | CRS: {target_crs}")

Performance Tuning & Fallback Routing

For regional or continental-scale deployments, in-memory allocation may exceed workstation limits. Implement the following fallback routing to maintain pipeline stability:

  • Windowed I/O: Partition the target grid into 1024×1024 tiles. Process each window independently using rasterio.windows.Window to cap peak RAM at ~2 GB.
  • Virtual Raster (VRT) Fallback: When disk I/O latency dominates, generate a pre-aligned VRT using rasterio.vrt.WarpedVRT. This defers resampling to read-time and eliminates intermediate array allocation.
  • GDAL Cache Tuning: Set GDAL_CACHEMAX to 25% of available RAM before execution. For Linux/macOS: export GDAL_CACHEMAX=8000. This accelerates tile reads during reprojection.
  • Resampling Kernel Selection: Use Resampling.average for PVGIS (reduces aliasing in high-frequency TMY data) and Resampling.bilinear for NASA POWER (preserves daily gradient continuity). Avoid nearest for irradiance modeling, as it introduces quantization artifacts in capacity factor calculations.

Refer to the official Rasterio Reprojection & Warping Documentation for kernel-specific performance benchmarks and memory footprint matrices.

Downstream Validation & Pipeline Integration

Post-stack validation must verify spatial integrity, nodata propagation, and metadata compliance before feeding arrays into PVLIB or SAM yield models. Treat it as a CI/CD gate identical in spirit to the input-side checks in Spatial Data Quality & Validation: the source rasters themselves should already have passed provenance and value-range checks such as those in validating NREL solar datasets with Python before they ever reach this stack.

python
def audit_stacked_raster(path):
    """Verify output integrity for project finance & CI/CD compliance."""
    with rasterio.open(path) as src:
        assert src.count == 2, "Expected 2 bands (POWER, PVGIS)"
        assert src.dtype == "float32", "Precision mismatch detected"
        assert src.crs.to_epsg() == 4326, "CRS drift detected"

        # Check for catastrophic nodata bleed.
        # Use np.isnan when nodata is NaN (NaN != NaN), otherwise use equality.
        b1 = src.read(1)
        b2 = src.read(2)
        nodata = src.nodata
        if nodata is not None and np.isnan(nodata):
            band1_mask = np.isnan(b1)
            band2_mask = np.isnan(b2)
        else:
            band1_mask = (b1 == nodata) if nodata is not None else np.zeros_like(b1, dtype=bool)
            band2_mask = (b2 == nodata) if nodata is not None else np.zeros_like(b2, dtype=bool)
        overlap_nodata = np.sum(band1_mask & band2_mask)
        logger.info(f"Nodata overlap: {overlap_nodata} pixels")

        # Log affine matrix for audit trail
        logger.info(f"Transform: {src.transform}")
        return True

Embed this validation step immediately after stack generation. It guarantees deterministic alignment across environment deployments (local, staging, cloud) and satisfies technical due diligence requirements for renewable asset financing. For advanced coordinate transformation troubleshooting and GDAL-level warp diagnostics, consult the GDAL Coordinate Transformation & Resampling Algorithms reference.

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