# Presentation: 170590

## Potential Reduction of Uncertainty in Precipitation Retrieval by Inclusion of Dynamical Constraints

**Wing-Yee (Hester) Leung,**
**Gregory J. Tripoli, Eric A. Smith,**
**and Alberto Mugnai**

## Passive Microwave Precipitation Retrieval

**Type of rainrate retrieval algorithm under study is physically-based, uses Cloud Radiation Database (CRD) as basis of relationship between TBs and rainrates, and employs Bayesian operator to find microphysical / rainrate profile solutions applied to database subsets.**

***A-priori***** database is composed of vertical microphysical profiles linked to surface rainrates from cloud resolving model (CRM) simulations.**
**Database TBs corresponding to simulated microphysical profiles are determined from passive microwave radiative transfer model.  **

## However, relationship between simulated microphysical
profiles and simulated multi-spectral TBs is not strictly unique.

**profiles and simulated multi-spectral TBs is not strictly unique.**

**Four different locations and different types of precipitation systems produce similar TBs.**
**During retrieval, given observed set of multispectral TBs, one can often match set of microphysical profiles with strongly differing precipitation outcomes.**

**Notes:**
UL: 08262005
UR: 09052005
LL:  06092006
LR: 06062006

## Cloud Dynamics and Radiation Database (CDRD)

**Additional information about synoptic conditions of simulations are added to database.**
**By applying Bayesian-based statistical estimation method, it is expected that use of additional constraints that describe precipitating environment can help in choosing more appropriate microphysical profiles during database mining process.**
**Thus, precipitation retrieval uncertainties can be reduced.**

## Cloud Dynamics and Radiation Database (CDRD) Approach

## Two Types of Dynamical and Thermodynamical Tags (parameters) Saved from CRM to Populate CDRD

**50 km Tags**
**describe regional synoptic conditions **
**obtained from global forecast models such as from ECMWF, GFS, etc.  **
**2 km Tags**
** ****topography information from real time**

## 50-km Tags

| Mean Sea Level Pressure (hPa) | Freezing Level (m) | Surface Theta Gradient (K km-1) | LFC Height (m) |
| --- | --- | --- | --- |
| Surface Temperature 
(K) | Lifted Index | 700 hPa Theta Gradient (K km-1) | LCL Height (m) |
| **U – wind (m s-1) |  Froude Number | Surface Theta-E Gradient (K km-1) | Topographic Height (m) |
| **V – wind (m s-1) | Surface Theta-E (K) | 700 hPa Theta-E Gradient (K km-1) | PBL Height (m) |
| Vertical U Momentum Flux (kg m-1 s-2) | Surface Brunt Vaisala Frequency (s-1) | **Q vector Convergence 
(m2s-1kg-1) | PBL Richardson # |
| Vertical V Momentum Flux (kg m-1 s-2) | **Temperature (K) | Divergence at Surf, 700, & 200 hPa (s-1) | Potential Vorticity Advection at 700 & 250 hPa |
| CIN (J kg-1) | Potential Vorticity at 700 & 200 hPa (PVU) | Height of Maximum CAPE (m) | Diabatic Moisting
(J kg-1 day-1) |
| Maximum CAPE 
(J kg-1) | Vertical Vorticity at Surf, 700, & 200 hPa (s-1) | Latent Heating
(K day-1) | 1000-850hPa Thickness (m) |
| Surface-based CAPE
 (J kg-1) | Theta-e Minimum (K) | 1000-500 hPa Thickness (m) | **Vertical Velocity 
(m s-1) |
| Kinetic Energy (J) | 0-6 km Wind Shear 
(ms-1m-1) | Surface Moisture Flux Convergence
 (g m-2 s-1) | Surface Pressure (hPa) |

## 2 km Tags

| Cloud Ceiling (m) | ** Temperature (K) |
| --- | --- |
| Topographic Height (m) | ** Specific Humidity  |
| Largest Topographic
Neighbor Difference (m) | Cloud Fraction |
| Direction of Topographic
Direction (degrees) | Convective Cloud Fraction |
| Mean Sea Level Pressure (hPa) | Stratiform Cloud Fraction |
| PBL Height (m) | Surface Pressure (hPa) |
| ** Vertical Velocity (m s-1) |  |

## Source of Simulations in Current CDRD 

**Source of Simulations in Current CDRD **

- Total Number of simulations: ~180
- Total Number of realizations: ~ 1.4 million
- 1105164 (1.1 million) profiles are over the ocean.
- 301281 (0.3 million) profiles are over land.

## Hypothesis

**Dynamical, Thermodynamical, and Hydrological (DTH) variables classification can narrow domain of appropriate microphysical profiles used for Bayesian estimation.**
**Variance of targeted microphysical profiles is smaller using CDRD approach relative to original CRD approach.**

## Problems

**Dataset (not yet complete)**
**bias in Mediterranean basin profiles**
**more ocean than land simulations**
**many more convective profiles than stratiform profiles**

## Liquid and Vapor Water Paths 

**Results suggest three (perhaps more) underlying dataset structures.**

## TB - Surface Rainrate Scatter Diagrams

**Multiple TB - Surface RR relationships are evident.**

**Scatter diagrams of all profiles over ocean within 35oS - 35oN latitude band**

## Adding surface temperature constraint of 24-26 oC narrows profiles domain, however clear TB-RR relationships are not evident. 

## Adding additional freezing level height constraint of 5.0-5.3 km further narrows profiles domain, with TB-RR relationships beginning to emerge.

## Altering freezing level height constraint to 5.3-5.7 km also narrows profile domain, while at same time producing even more coherent TB-RR relationships.

## Finally, returning freezing level height constraint to 5.0-5.3 km while adding third LWP constraint of 59-62 g m-2 produces different but also coherent TB-RR relationships.

## Relationships Between TBs and LWPs

**Scatter diagrams of all profiles over ocean between 35oS - 35oN.**

## Add surface temperature constraint of 21-22 oC.

**Add surface temperature constraint of 21-22 oC.**

## Sharpen constraints:

**Sharpen constraints:**
**surface temperature of**
**	****21-22 oC**
**freezing level height of**
**	****4.5-5.0 km **

## Change constraints:

**Change constraints:**
**surface temperature of**
**	****21-22 oC**
**freezing level height of**
**	****5.0-5.5 km**

## Change constraints again:

**Change constraints again:**
**surface temperature of**
** ****	****21-22 oC**
**freezing level height of**
**	****5.5–6.0 km**

## Generate more simulations intending to produce uniform global distribution of profiles while at same time generating many more stratiform profiles.
Determine quantitatively by thorough analysis of  CDRD profile database, degree to which uncertainty in precipitation estimation can be reduced through including DTH constraints.
Ultimate goal is to determine how to best apply DTH constraints so as to significantly reduce variance in simulated frequency-dependent TB-RR relationships used in PMW precipitation retrieval.

**Determine quantitatively by thorough analysis of  CDRD profile database, degree to which uncertainty in precipitation estimation can be reduced through including DTH constraints.**
**Ultimate goal is to determine how to best apply DTH constraints so as to significantly reduce variance in simulated frequency-dependent TB-RR relationships used in PMW precipitation retrieval.**

**Future Research**

## Thank You

## Backup Slides

## Current Issues

- It can be seen even with greater LWPs, TBs at lower frequencies are highly correlated with surface temperatures.
- It is not sure by how much that surface emissivities that is calculated in radiative transfer model together with surface temperatures that affect TBs outcome.
- More investigation on how the radiative transfer model works for a liquid saturated atmospheric column, particularly why the model can calculate TBs that is greatly in correlation with surface temperatures under those conditions.