Comparison of aircraft-derived observations with in situ research aircraft measurements

Mode Selective Enhanced Surveillance (Mode-S EHS) reports are aircraft-based observations that have value in numerical weather prediction (NWP). These reports contain the aircraft's state vector in terms of its speed, direction, altitude and Mach number. Using the state vector, meteorological observations of temperature and horizontal wind can be derived. However, Mode-S EHS processing reduces the precision of the state vector from 16-bit to 10-bit binary representation. We use full precision data from research grade instruments, on-board the United Kingdom's Facility for Atmospheric Airborne Measurements, to emulate Mode-S EHS reports and to compare with derived observations. We aim to understand the observation errors due to the reduced precision of Mode-S EHS reports. We derive error models to estimate these observation errors. The temperature error increases from 1.25 K to 2.5 K between an altitude of 10 km and the surface due to its dependency on Mach number and also Mode-S EHS precision. For the cases studied, the zonal wind error is around 0.50 ms− 1 and the meridional wind error is 0.25 ms− 1. The wind is also subject to systematic errors that are directionally dependent. We conclude that Mode-S EHS derived horizontal winds are suitable for data assimilation in high-resolution NWP. Temperature reports may be usable when aggregated from multiple aircraft. While these reduced precision, high frequency data provide useful, albeit noisy, observations; direct reports of the higher precision data would be preferable

Initialization and ensemble generation for decadal climate predictions: A comparison of different methods

Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter (EnKF), the filtered anomaly initialization (FAI) and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter (EDF) corrects each ensemble member with the ensemble mean during model integration. And the bred vectors (BV) perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the EnKF and FAI show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the BV, the EDF and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability, and with respect to the temporal evolution at the 26° N latitude