Cloud products for Himawari-8/9

Coordination Group for Meteorological Satellites ‐ CGMS
Cloud products for
Himawari-8/9
Presented to CGMS-42 [Working Group II] session, agenda item [II/8]
Japan Meteorological Agency
Japan Meteorological Agency, May 2014
Coordination Group for Meteorological Satellites ‐ CGMS
Cloud Products for Himawari‐8/9
Introduction
• JMA is currently developing two types of cloud products for Himawari‐8/9
– Empirical method
– Physics based method (Optimal Cloud Analysis, OCA)
• Developing a new product to monitor rapidly developing cumulous using simulated satellite imagery
Japan Meteorological Agency, May 2014
Coordination Group for Meteorological Satellites ‐ CGMS
Cloud Products for Himawari‐8/9
Empirical method
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Elements: cloud mask, Cloud type/phase, Cloud top height
User: L2 product developers in JMA
Algorithm: based on NWC‐SAF and NOAA/NESDIS
Status: Prototypes available, tuning for Himawari‐8/9
Quality of prototype using SEVIRI against MODIS product: – Cloud mask: 85%
– Cloud phase: 97%
– Cloud top height bias: ‐0.1~0.3 km(opaque), 2.4km(semi‐transparent)
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JMA joined CREW inter comparison
mask
Japan Meteorological Agency, May 2014
phase
(1200UTC 13th Jun. 2008)
height
RGB
Coordination Group for Meteorological Satellites ‐ CGMS
Cloud Products for Himawari‐8/9
Physics based method (Optimal Cloud Analysis)
• Thanks to EUMETSAT kind cooperation, JMA implements the Optimal Cloud Analysis (OCA) for Himawari‐8/9 • Previous study on SEVIRI data as a proxy of AHI data in JMA system seems successful
• Preparation for AHI data is ongoing
Japan Meteorological Agency, May 2014
Coordination Group for Meteorological Satellites ‐ CGMS
Development of a new product using simulated satellite imagery for convective cloud monitoring Status
Rapid developing cumulous area(RDCA)
・Investigate a possibility of retrieving physical parameter related to RDCA from satellite imagery
・Predictive detection of developing cumulous cloud from Himawari‐8
Simulated convective clouds from NWP model
UPDATE
Distinguish whether developing or decaying from satellite data.
 JMA provides RDCA information using MTSAT‐1R rapid scan data.
 Excessive detection;
RDCA detects weak developing cumulous cloud.
・Simulated Atmospheric condition Radiative Transfer Model
・Simulated Himawari‐8 images
Japan Meteorological Agency, May 2014
Coordination Group for Meteorological Satellites ‐ CGMS
Development of a new product using simulated satellite imagery for convective cloud monitoring ～ Case study
Stage of early convection
Graupel mixture ratio (cross‐section from NWP)
BT(10.4 μm, from RTM)
Stage of mature convection
Graupel mixture ratio (cross‐section from NWP)
Graupel
generation
VIS/NIR
IR
Japan Meteorological Agency, May 2014
BT(10.4 μm, from RTM)
Coordination Group for Meteorological Satellites ‐ CGMS
Summary
• Based on the review in CREW, JMA’s empirical algorithm will be into “operational” with an additional improvement of its accuracy as a “starter” after Himawari‐8 operation
• Future transition to physics based algorithm (OCA) for retrieving cloud parameters is also considered in JMA/MSC by the assistance of EUMETSAT and NOAA/NESDIS
• Investigation based on simulation data from NWP and RTM is ongoing in order to improve RDCA product
• A “diagnostic” database of ground surface radiative characteristics for improving cloud masking is under development and will be introduced in operation
• JMA/MSC will be glad to join the discussion on the common algorithm development in CREW (ICWG)
Japan Meteorological Agency, May 2014