nwood_igarss_2011_rev2.pdf
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This document discusses combining space-based active microwave (CloudSat) and passive microwave (PMW) observations to improve global snowfall estimates. CloudSat uses radar to detect snowfall but has limitations without additional data. PMW provides constraints by relating brightness temperature depression to ice water path. The authors aim to use optimal estimation to relate CloudSat radar reflectivity measurements to snowfall rate, incorporating prior particle properties from ground observations. Combined CloudSat and PMW data can help determine key retrieval parameters like snow size distribution and fall speed.
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4) The
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Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
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- 1. Combining Space-based Active and Passive Microwave Observations to Improve Global Snowfall Estimates Tristan L'Ecuyer Norman Wood University of Wisconsin Colorado State University Madison, Wisconsin Fort Collins, Colorado Acknowledgements: John Haynes, CSU CIRA; Peter Rodriguez and David Hudak, EC; Andy Heymsfeld, NCAR; Larry Bliven, NASA GSFC; Gwo-Jong Huang, CSU
- 2. Outline Detecting snowfall from CloudSat CloudSat's radar-only retrieval Limitations of refectivity observations a priori information Retrieval performance characteristics Constraints from PMW observations
- 3. CloudSat Observations CloudSat and CPR Parameters 94 GHz (3.2 mm, W-band) Inclination: 98 degrees Vertical resolution: 485 m Footprint: 1.7 x 1.4 km Sensitivity: -28 dBZ
- 4. Snow detection: near-surface refectivity, temperature profle, and a melting layer model (2C-PRECIP-COLUMN, J. Haynes) Ze(z): Precip layer? T(z): Melting level height fmelt at surface: Ze(0): > -7 dBZe, snow certain > -15 dBZe, snow likely
- 5. The refectivity-based retrieval problem is underconstrained
- 6. Optimal estimation relates Ze to P and incorporates prior information
- 7. A priori particle properties are developed from intensive ground-based observations Stratiform Lake Efect Observations: Snow size distribution Size-resolved fallspeed X-band Ze Precip. rate
- 8. Results provide parameter distributions and constraints on scattering properties DDA “habits” constructed using Composite PDF, e.g. m(D) and Ap(D) ln(gamma) vs ln(alpha)
- 9. Snowfall rates are produced from retrieved N0, lambda + a priori particle model
- 10. Measurements contribute primarily to determination of lambda lambda N0 Forward Model Sensitivities A-matrix Diagonals
- 11. Constraint provided by coincident PMW observations... Tb depression vs IWP Simulations w/ homogeneous Precipitation layer Liu & Curry, 1996 - Snowstorm simulation with a cloud-resolving model + PMW simulator (W. Berg)