diff --git a/imap_processing/hi/hi_l2.py b/imap_processing/hi/hi_l2.py
index e090c4a843..08c250129e 100644
--- a/imap_processing/hi/hi_l2.py
+++ b/imap_processing/hi/hi_l2.py
@@ -305,7 +305,11 @@ def calculate_ena_intensity(
     map_ds["ena_intensity_stat_uncert"] = (
         map_ds["ena_signal_rate_stat_unc"] / flux_conversion_divisor
     )
-    map_ds["ena_intensity_sys_err"] = map_ds["bg_rates_unc"] / flux_conversion_divisor
+    map_ds["ena_intensity_sys_err"] = (
+        np.sqrt(map_ds["bg_rates"] * map_ds["exposure_factor"])
+        / map_ds["exposure_factor"]
+        / flux_conversion_divisor
+    )
 
     # Combine calibration products using proper weighted averaging
     # as described in Hi Algorithm Document Section 3.1.2
@@ -367,16 +371,11 @@ def combine_calibration_products(
         map_ds, geometric_factors, esa_energies
     )
 
-    # Calculate total variance
-    # Note that sys_err contains uncertainty, so it must be squared to get
-    # the systematic variance needed in this equation.
-    total_variance = improved_stat_variance + sys_err**2
-
     # Perform inverse-variance weighted averaging
     # Handle divide by zero and invalid values
     with np.errstate(divide="ignore", invalid="ignore"):
         # Use total variance weights for flux combination
-        flux_weights = 1.0 / total_variance
+        flux_weights = 1.0 / improved_stat_variance
         weighted_flux_sum = (ena_flux * flux_weights).sum(dim="calibration_prod")
         combined_flux = weighted_flux_sum / flux_weights.sum(dim="calibration_prod")
 
@@ -450,11 +449,6 @@ def _calculate_improved_stat_variance(
     # Total count rates for Poisson uncertainty calculation
     total_count_rates_for_uncertainty = map_ds["bg_rates"] + averaged_signal_rates
 
-    # Ensure non-negative values for sqrt and minimum of 1 for uncertainty calculation
-    total_count_rates_for_uncertainty = xr.where(
-        total_count_rates_for_uncertainty < 1, 1, total_count_rates_for_uncertainty
-    )
-
     logger.debug("Computing improved flux uncertainties")
     # Statistical variance:
     with np.errstate(divide="ignore", invalid="ignore"):
diff --git a/imap_processing/tests/hi/test_hi_l2.py b/imap_processing/tests/hi/test_hi_l2.py
index 215ca2bfa0..1ada7f6f1b 100644
--- a/imap_processing/tests/hi/test_hi_l2.py
+++ b/imap_processing/tests/hi/test_hi_l2.py
@@ -644,7 +644,7 @@ def test_statistical_uncertainty_combination_correctness():
     # Manual calculation of expected statistical uncertainty combination
     # combined_stat_unc = sqrt(1/sum(1 / stat_unc**2))
     expected_combined_stat_unc = np.sqrt(1 / np.sum(1 / stat_unc_values**2))
-    flux_weights = 1.0 / (np.array([101, 101]) + np.array([4, 16]))
+    flux_weights = 1.0 / np.array([101, 101])
     expected_flux = np.sum(flux_values.squeeze() * flux_weights) / np.sum(flux_weights)
 
     np.testing.assert_almost_equal(