Evaluation of the Near Real-Time Forecasts Using a Global Nonhydrostatic Model during the CINDY2011/DYNAMO

Abstract

By comparison with satellite and field observations, the comprehensive performance and potential utility of near real-time forecasts using Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are demonstrated by exploiting the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011) / Dynamics of the Madden-Julian Oscillation (DYNAMO) campaign. A week-long forecast was run each day using a regionally stretched version of NICAM, with the finest mesh size of 14 km over the tropical Indian Ocean (IO), throughout the intensive observation period (IOP).The simulated precipitation time series fairly represented the evolution and propagation of the observed Madden-Julian Oscillation (MJO) events, although a 30 % overprediction of precipitation over the IO domain (60 -90°E, 10°S -10°N) was found on average. Frequencies of strong (> 40 mm day −1 ) precipitation were overpredicted, while those of weak precipitation were underpredicted against satellite observations. Compared with the field observations at Gan Island, the biases in precipitation frequency were less obvious, whereas the growth of lower to middle tropospheric dry (~ 1 g kg −1) and warm (~ 1 K) biases were found. Despite these mean biases, temporal variations of the moisture and zonal wind profiles including the MJO events were reasonably simulated.Using the forecast data the moisture and energy budgets during the IOP were investigated. The diagnosis using the 7-day-mean fields captured the observed features of the MJO events. Meanwhile, significant upward transport of moisture by the grid-resolved high-frequency variability was detected throughout the IOP. The relationship between these high-frequency effects and the simulated MJO or mean biases is also discussed.

Interaction between the MJO and High-Frequency Waves over the Maritime Continent in Boreal Winter

“…In the preconditioning phase (e.g., day 26 in the MC or days 26 to 0 in 1408E-1808), positive anomalies of e Q 2_LH and e Q 2_T-LI are formed in the lower to midtroposphere. These HFW effects on the MJO are consistent with those found in the previous studies (e.g., Nasuno et al 2015Nasuno et al , 2017.…”

Impacts of Cloud Microphysics Modifications on Diurnal Convection and the ISO over the Maritime Continent: A Case Study of YMC-Sumatra 2017

“…Following Nasuno et al (2017) and Nasuno (2019), the 5-day outputs were combined to create time series covering the entire period (i.e., averaging outputs initialized on different dates for each valid time), and horizontal and vertical components of moisture advection terms were calculated (Supplement 3) using these time series; the ISO-scale components were extracted using a 7-day running average. For comparisons, precipitation data from the Climate Prediction Center morphing method (CMORPH) products (Joyce et al 2004) and atmospheric data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-Interim and ERA5; Dee et al 2011;C3S, 2017) were used.…”

“…The essential role of the moisture variability in the ISO dynamics has been well established (Fuchs and Raymond 2005;Raymond and Fuchs 2009;Sobel et al 2001;Sobel and Maloney 2013;Nasuno et al 2015Nasuno et al , 2017. In this study, sum of the horizontal and vertical components of the moisture advection terms [Eq.…”

“…In addition, scale interactions in regional models are restricted by their domains. With this respect, global cloudpermitting models have an advantage (Miura et al 2007(Miura et al , 2015Miyakawa et al 2014;Fujita et al 2011;Nasuno et al 2017;Nasuno 2019).…”

The Impact of Hybrid Usage of a Cumulus Parameterization Scheme on Tropical Convection and Large‐Scale Circulations in a Global Cloud‐System Resolving Model

“…Many high-resolution modeling studies have attempted to address this issue, by using regional (Ban et al, 2014;Grell et al, 2000;Kendon et al, 2014;Prein et al, 2013, etc. ), global/near-global (e.g., Bretherton & Khairoutdinov, 2015;Miyamoto et al, 2013;Putman & Suarez, 2011;Satoh et al, 2014), and stretched-grid (e.g., Hagos et al, 2013;Nasuno et al, 2017;Skamarock et al, 2012) cloud-permitting models, as well as superparameterization (Benedict & Randall, 2009;Grabowski & Smolarkiewicz, 1999;Khairoutdinov & Randall, 2001;Pritchard et al, 2011), as summarized in Prein et al (2015). Explicit representation of convection at higher resolutions tends to produce certain preferable convective features.…”