Fix: typos

Author: heptaflar

ABOUT.md

@@ -56,7 +56,7 @@ as large, evolving software systems. In many modern applications—remote
 sensing retrieval chains, satellite calibration pipelines, high‑resolution
 climate data processing—the “measurement device” is not a static laboratory
 instrument with a simple response model, but a complex codebase that changes
-as algorithms, parameterisations, and dependencies are updated. The genuine
+as algorithms, parameterization, and dependencies are updated. The genuine
 forward map from inputs to outputs is whatever the current version of the
 code computes. Maintaining an analytical model and its hand‑derived Jacobians
 in sync with such a code is labour‑intensive and error‑prone, and often not

README.md

@@ -19,7 +19,7 @@ objects rather than forcing the uncertainty calculus into a fixed set of
 closed‑form formulas.
 
 The ideas presented here grew out of earlier project-specific implementations
-of AD-based uncertainty propagation for harmonised satellite calibration
+of AD-based uncertainty propagation for harmonized satellite calibration
 workflows underpinning fundamental climate data records.
 
 ## Synopsis
@@ -37,7 +37,7 @@ deliver 3D data, and Earth climate records form 4D datasets—with ocean
 and atmosphere data reaching up to 5D. Applying standard matrix-based
 uncertainty propagation requires flattening these N-D arrays into 1D
 vectors, which obscures the vital spatiotemporal structure of both the
-data and the algorithms designed to analyze it. Tensors are the ideal
+data and the algorithms designed to analyse it. Tensors are the ideal
 solution, and the law of propagation of uncertainty, when formulated
 and coded in general tensor form, is elegantly beautiful. If you’re curious,
 compare [NIST TN 1297](https://www.nist.gov/pml/nist-technical-note-1297/nist-tn-1297-appendix-law-propagation-uncertainty) (Equation A-3)