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delivered to the backend database in the same way for both SADIS 2G and Secure SADIS FTP (there are 2 different types of input channel process which store data into the same database). Therefore, the evaluation can be considered compatible with both the SADIS 2G and Secure SADIS FTP implementations

and Secure SADIS FTP (there are 2 different types of input channel process which store data into the same database). Therefore, the evaluation can be considered compatible with both the SADIS 2G and Secure SADIS FTP implementations of the system. Software Functionality Available and Compliant? Comments

to'). Step 6. Write an overall narrative for your Theory of Change Step 2. Define the long-term impact Step 5. Identify interventions needed to create desired change Step 3. Understand and map the change process Step 4. Make your assumptions explicit Adapted from DEFRA ToC tool (2022) Two ToC

estimate. September Monthly Averaged Extent Projection: (4.1 ± 1.0) × 10 6 km 2 Model: HadGEM3 Ice Component: CICE [Hunke and Dukowicz, 2002, Hunke and Lipscomb, 2010], Global Sea Ice 3.0 Ocean Component: NEMO [Madec, 2008], Global Ocean 3.0 Atmospheric Component: Met Office Unified Model (UM

based estimate. September Monthly Averaged Extent Projection: (5.3 ± 0.6) × 10 6 km 2 Model: HadGEM3 Ice Component: CICE [Hunke and Lipscomb, 2010], Global Sea Ice 3.0 Ocean Component: NEMO [Madec, 2008], Global Ocean 3.0 Atmospheric Component: Met Office Unified Model (UM) [Brown et al., 2012

Maintenance experience with SSPA dual-polarization weather radar Morihiro SAWADA, Y. KAJIWARA, H. OKUMURA, H. Inoue, and T. HIKIDA Observation Division, Atmosphere and Ocean Department, JMA 9th Nov. 2023 Japan Meteorological Agency Contents 1. SSPA weather radar in Japan 2. Efficient inspection

η η η Spatial Extremal Dependence in Natural Hazard Footprints Laura C. Dawkins 1 & David B. Stephenson 2 1. Motivation Storm damage 2. Application • Re-insurers use catastrophe models to estimate risk of natural hazards for insurance pricing. These models require synthetic simulations of natural

will be used with existing depth duration frequency (DDF) models, such as the Flood Estimation Handbook 2013 DDF model 1 , which give expected rainfall depths for different durations and annual exceedance probabilities based on present day observed rainfall (see Figure 2). The annual exceedance probability

Can we tune parametrisations to reproduce relationships that control ENSO? Mike Bell, Benni Buchenau, John Edwards (Met Office) Mike Benni 1. Introduction Strong correlations (box 2) : Interannual variations in the net surface heat flux (NSHF) into the equatorial Pacific ocean are well correlated

0.7 1.3 1.4 Country Channel Islands mean annual temperature (°C) 2020-2039 RCP8.5 0.2 0.3 0.9 1.5 1.7 Country Channel Islands mean annual temperature (°C) 2030-2049 SRES A1B 0.2 0.4 1 1.7 1.9 Country Channel Islands mean annual temperature (°C) 2030-2049 RCP2.6 0.2 0.4 1.1 1.8 2 Country Channel