diff --git a/pyproject.toml b/pyproject.toml
index 79bdecd..9d15fd9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "radclss"
-version = "2026.5.20"
+version = "2026.7.1"
 description = "Extracted Radar Columns and In Situ Sensors"
 readme = "README.md"
 requires-python = ">=3.10"
diff --git a/src/radclss/config/default_config.py b/src/radclss/config/default_config.py
index 9a7603a..c159046 100644
--- a/src/radclss/config/default_config.py
+++ b/src/radclss/config/default_config.py
@@ -76,6 +76,24 @@
         "signal_to_noise_ratio_copolar_v",
         "normalized_coherent_power",
         "normalized_coherent_power_v",
+    ],
+     "radar_xsapr": [
+        "classification_mask",
+        "censor_mask",
+        "uncorrected_copol_correlation_coeff",
+        "uncorrected_differential_phase",
+        "uncorrected_differential_reflectivity",
+        "uncorrected_differential_reflectivity_lag_1",
+        "uncorrected_mean_doppler_velocity_h",
+        "uncorrected_mean_doppler_velocity_v",
+        "uncorrected_reflectivity_h",
+        "uncorrected_reflectivity_v",
+        "uncorrected_spectral_width_h",
+        "uncorrected_spectral_width_v",
+        "signal_to_noise_ratio_copolar_v",
+        "normalized_coherent_power",
+        "normalized_coherent_power_v",
+        "radar_echo_classification"
     ],
     "met": [
         "base_time",
diff --git a/src/radclss/core/radclss_core.py b/src/radclss/core/radclss_core.py
index 808eaf0..0b039c6 100644
--- a/src/radclss/core/radclss_core.py
+++ b/src/radclss/core/radclss_core.py
@@ -4,6 +4,7 @@
 import act
 import numpy as np
 import pandas as pd
+import traceback
 
 
 from ..util.column_utils import (
@@ -203,6 +204,8 @@ def radclss(
 
                     except Exception:
                         result = None
+                        if verbose:
+                            traceback.print_exc()
                     if verbose:
                         if result is not None:
                             print(
@@ -303,7 +306,7 @@ def _get_nexrad_wrapper(time_str):
                     output_config["site"],
                     input_site_dict,
                     nexrad_radar=nexrad_site,
-                    height_bins=height_bins
+                    height_bins=height_bins,
                 )
 
             results = current_client.map(_get_nexrad_wrapper, time_list)
@@ -340,7 +343,7 @@ def _get_nexrad_wrapper(time_str):
                     output_config["site"],
                     input_site_dict,
                     nexrad_radar=nexrad_site,
-                    height_bins=height_bins
+                    height_bins=height_bins,
                 )
 
             successful_count = 0
@@ -436,7 +439,7 @@ def _get_nexrad_wrapper(time_str):
         print("=" * 80)
         print(f"  Time coordinate method: {time_coords}")
 
-    ds_concat[k] = ds_concat[k].drop_duplicates('time')
+    ds_concat[k] = ds_concat[k].drop_duplicates("time")
     if "radar" in time_coords:
         if verbose:
             print(f"  Reindexing all datasets to {time_coords} time coordinates")
@@ -710,7 +713,7 @@ def _get_nexrad_wrapper(time_str):
                 site = base_station
             site = site.upper()
 
-            if instrument == "kazr2":
+            if instrument == "kazr2" or instrument == "kazr":
                 _instrument_tasks.append(
                     (
                         k,
diff --git a/src/radclss/util/column_utils.py b/src/radclss/util/column_utils.py
index 36cbe83..aee75bc 100644
--- a/src/radclss/util/column_utils.py
+++ b/src/radclss/util/column_utils.py
@@ -150,6 +150,7 @@ def _vpt_to_column_timeseries(radar, height_bins):
     ]
     data_vars = {}
     for key in radar.fields:
+        print(key, radar.fields[key]["data"].dtype)
         arr = np.ma.filled(radar.fields[key]["data"], np.nan).astype(float)
         attrs = {
             tag: radar.fields[key][tag] for tag in da_meta if tag in radar.fields[key]
@@ -518,7 +519,6 @@ def subset_points(
             for lat, lon, site in zip(lats, lons, sites):
                 # Make sure we are interpolating from the radar's location above sea level
                 # NOTE: interpolating throughout Troposphere to match sonde to in the future
-
                 if "vpt" in radar.metadata["scan_mode"]:
                     if radar.metadata.get("facility_id", "") == site:
                         da = _vpt_to_column_timeseries(radar, height_bins)
@@ -563,27 +563,27 @@ def subset_points(
                 da = da.sortby("height")
                 valid = np.isfinite(da["height"])
                 n_valid = int(valid.sum())
+                interpolated = False
+                dvars = da.data_vars
+                for v in dvars:
+                    if np.all(np.isnan(da[v].values)):
+                        da = da.drop(v)
                 if n_valid > 0:
-                    try:
-                        # Drop all NaNs
-                        da = (
-                            da.dropna("height")
-                            .sortby("height")
-                            .interp(height=height_bins)
-                        )
-                    except pd.errors.InvalidIndexError:
-                        da = da.drop_duplicates("height", keep="first")
-
-                        valid = np.isfinite(da["height"])
-                        da = (
-                            da.dropna("height")
-                            .sortby("height")
-                            .interp(height=height_bins)
-                        )
-                        time_offset = time_offset.drop_duplicates(
-                            "height", keep="first"
-                        )
-                else:
+                    da_clean = da.dropna("height").sortby("height")
+                    if da_clean.sizes.get("height", 0) > 0:
+                        try:
+                            da = da_clean.interp(height=height_bins)
+                        except pd.errors.InvalidIndexError:
+                            da_clean = da_clean.drop_duplicates(
+                                "height", keep="first"
+                            )
+                            da = da_clean.interp(height=height_bins)
+                            time_offset = time_offset.drop_duplicates(
+                                "height", keep="first"
+                            )
+                        interpolated = True
+                
+                if not interpolated:
                     target_height = xr.DataArray(
                         height_bins, dims="height", name="height"
                     )